2023

A Survey on Large Language Models for Software Engineering

Zhang, Quanjun, Fang, Chunrong, Xie, Yang et al.

Understand

Software Engineering (SE) is the systematic design, development, maintenance, and management of software applications underpinning the digital infrastructure of our modern world.

Very recently, the SE community has seen a rapidly increasing number of techniques employing Large Language Models (LLMs) to automate a broad range of SE tasks.
Nevertheless, existing information of the applications, effects, and possible limitations of LLMs within SE is still not well-studied.
In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the LLM-based SE community.

Built on

M. V. Zelkowitz, “Perspectives in software engineering,” ACM Computing Surveys (CSUR) , vol. 10, no. 2, pp. 197–216, 1978
1978
Earlier work this paper cites.
J. Biolchini, P. G. Mian, A. C. C. Natali, and G. H. Travassos, “Systematic review in software engineering,” System engineering and computer science department COPPE/UFRJ, Technical Report ES , vol. 679, no. 05, p. 45, 2005
2005
Earlier work this paper cites.
H. Zhang, M. A. Babar, and P. Tell, “Identifying relevant studies in software engineering,” Information and Software Technology (IST) , vol. 53, no. 6, pp. 625–637, 2011
2011
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
C. Niu, C. Li, V. Ng, J. Ge, L. Huang, and B. Luo, “Spt-code: sequence-to-sequence pre-training for learning source code representations,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2006–2018
2018
Earlier work this paper cites.
Z. Fan, H. Ruan, S. Mechtaev, and A. Roychoudhury, “Oracle-guided program selection from large language models,” 2018
2018
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang et al. , “Codebert: A pre-trained model for programming and natural languages,” in Findings of the Association for Computational Linguistics: EMNLP 2020 , 2020, pp. 1536–1547
2020
Earlier work this paper cites.
Z. Wang, M. Yan, J. Chen, S. Liu, and D. Zhang, “Deep learning library testing via effective model generation,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 788–799
2020
Earlier work this paper cites.
A. Kanade, P. Maniatis, G. Balakrishnan, and K. Shi, “Learning and evaluating contextual embedding of source code,” in International conference on machine learning . PMLR, 2020, pp. 5110–5121
2020
Earlier work this paper cites.
D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan, A. Svyatkovskiy, S. Fu et al. , “Graphcodebert: Pre-training code representations with data flow,” arXiv preprint arXiv:2009.08366 , 2020
Original
2020
Earlier work this paper cites.
C. B. Clement, D. Drain, J. Timcheck, A. Svyatkovskiy, and N. Sundaresan, “Pymt5: multi-mode translation of natural language and python code with transformers,” arXiv preprint arXiv:2010.03150 , 2020
Original
2020
Earlier work this paper cites.
A. Svyatkovskiy, S. K. Deng, S. Fu, and N. Sundaresan, “Intellicode compose: Code generation using transformer,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 1433–1443
2020
Earlier work this paper cites.
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, “Electra: Pre-training text encoders as discriminators rather than generators,” arXiv preprint arXiv:2003.10555 , 2020
Original
2020
Earlier work this paper cites.
T. Hey, J. Keim, A. Koziolek, and W. F. Tichy, “Norbert: Transfer learning for requirements classification,” in 2020 IEEE 28th International Requirements Engineering Conference (RE) , 2020, pp. 169–179
2020
Earlier work this paper cites.
P. He, C. Meister, and Z. Su, “Structure-invariant testing for machine translation,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering , 2020, pp. 961–973
2020
Earlier work this paper cites.
S. Gupta, P. He, C. Meister, and Z. Su, “Machine translation testing via pathological invariance,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 863–875
2020
Earlier work this paper cites.
H. Tian, K. Liu, A. K. Kaboré, A. Koyuncu, L. Li, J. Klein, and T. F. Bissyandé, “Evaluating representation learning of code changes for predicting patch correctness in program repair,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 981–992
2020
Earlier work this paper cites.
J. M. Zhang, M. Harman, L. Ma, and Y. Liu, “Machine learning testing: Survey, landscapes and horizons,” IEEE Transactions on Software Engineering , vol. 48, no. 1, pp. 1–36, 2020
2020
Earlier work this paper cites.
Z. Yu, R. Cao, Q. Tang, S. Nie, J. Huang, and S. Wu, “Order matters: Semantic-aware neural networks for binary code similarity detection,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 01, 2020, pp. 1145–1152
2020
Earlier work this paper cites.
M. Tufano, D. Drain, A. Svyatkovskiy, S. K. Deng, and N. Sundaresan, “Unit test case generation with transformers and focal context,” arXiv preprint arXiv:2009.05617 , 2020
Original
2020
Earlier work this paper cites.
E. Biswas, M. E. Karabulut, L. Pollock, and K. Vijay-Shanker, “Achieving reliable sentiment analysis in the software engineering domain using bert,” in 2020 IEEE International conference on software maintenance and evolution (ICSME) . IEEE, 2020, pp. 162–173
2020
Earlier work this paper cites.
T. Zhang, B. Xu, F. Thung, S. A. Haryono, D. Lo, and L. Jiang, “Sentiment analysis for software engineering: How far can pre-trained transformer models go?” in 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2020, pp. 70–80
2020
Earlier work this paper cites.
Y. Wang, W. Wang, S. Joty, and S. C. Hoi, “Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP’21) , 2021, pp. 8696–8708
2021
Earlier work this paper cites.
A. Mastropaolo, S. Scalabrino, N. Cooper, D. N. Palacio, D. Poshyvanyk, R. Oliveto, and G. Bavota, “Studying the usage of text-to-text transfer transformer to support code-related tasks,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 336–347
2021
Earlier work this paper cites.
W. U. Ahmad, S. Chakraborty, B. Ray, and K.-W. Chang, “Unified pre-training for program understanding and generation,” arXiv preprint arXiv:2103.06333 , 2021
Original
2021
Earlier work this paper cites.
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. B. Clement, D. Drain, D. Jiang, D. Tang, G. Li, L. Zhou, L. Shou, L. Zhou, M. Tufano, M. Gong, M. Zhou, N. Duan, N. Sundaresan, S. K. Deng, S. Fu, and S. Liu, “Codexglue: A machine learning benchmark dataset for code understanding and generation,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, NeurIPS Datasets and Benchmarks 2021, December 2021, virtual , J. Vanschoren and S. Yeung, Eds., 2021
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv preprint arXiv:2107.03374 , 2021
Original
2021
Earlier work this paper cites.
J. Lin, Y. Liu, Q. Zeng, M. Jiang, and J. Cleland-Huang, “Traceability transformed: Generating more accurate links with pre-trained bert models,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 324–335
2021
Earlier work this paper cites.
A. Mastropaolo, E. Aghajani, L. Pascarella, and G. Bavota, “An empirical study on code comment completion,” in 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2021, pp. 159–170
2021
Earlier work this paper cites.
M. Ciniselli, N. Cooper, L. Pascarella, D. Poshyvanyk, M. Di Penta, and G. Bavota, “An empirical study on the usage of bert models for code completion,” in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . IEEE, 2021, pp. 108–119
2021
Earlier work this paper cites.
M. Ciniselli, N. Cooper, L. Pascarella, A. Mastropaolo, E. Aghajani, D. Poshyvanyk, M. Di Penta, and G. Bavota, “An empirical study on the usage of transformer models for code completion,” IEEE Transactions on Software Engineering , vol. 48, no. 12, pp. 4818–4837, 2021
2021
Earlier work this paper cites.
J. Y. Khan, M. T. I. Khondaker, G. Uddin, and A. Iqbal, “Automatic detection of five api documentation smells: Practitioners’ perspectives,” in 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2021, pp. 318–329
2021
Earlier work this paper cites.
Z. Zhu, Y. Wang, and Y. Li, “Trobo: A novel deep transfer model for enhancing cross-project bug localization,” in Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part I . Springer, 2021, pp. 529–541
2021
Earlier work this paper cites.
S. Chakraborty, R. Krishna, Y. Ding, and B. Ray, “Deep learning based vulnerability detection: Are we there yet,” IEEE Transactions on Software Engineering , 2021
2021
Earlier work this paper cites.
Y. Li, S. Wang, and T. N. Nguyen, “Vulnerability detection with fine-grained interpretations,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 292–303
2021
Earlier work this paper cites.
Z. Liu, Y. Feng, and Z. Chen, “Dialtest: automated testing for recurrent-neural-network-driven dialogue systems,” in Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2021, pp. 115–126
2021
Earlier work this paper cites.
N. Jiang, T. Lutellier, and L. Tan, “Cure: Code-aware neural machine translation for automatic program repair,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 1161–1173
2021
Earlier work this paper cites.
D. Drain, C. Wu, A. Svyatkovskiy, and N. Sundaresan, “Generating bug-fixes using pretrained transformers,” in Proceedings of the 5th ACM SIGPLAN International Symposium on Machine Programming , 2021, pp. 1–8
2021
Earlier work this paper cites.
B. Berabi, J. He, V. Raychev, and M. Vechev, “Tfix: Learning to fix coding errors with a text-to-text transformer,” in International Conference on Machine Learning . PMLR, 2021, pp. 780–791
2021
Earlier work this paper cites.
T. H. Jung, “Commitbert: Commit message generation using pre-trained programming language model,” in Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021) , 2021, pp. 26–33
2021
Earlier work this paper cites.
R. Schuster, C. Song, E. Tromer, and V. Shmatikov, “You autocomplete me: Poisoning vulnerabilities in neural code completion,” in 30th USENIX Security Symposium (USENIX Security 21) , 2021, pp. 1559–1575
2021
Earlier work this paper cites.
J. A. Prenner and R. Robbes, “Making the most of small software engineering datasets with modern machine learning,” IEEE Transactions on Software Engineering , vol. 48, no. 12, pp. 5050–5067, 2021
2021
Earlier work this paper cites.
J. Li, R. Huang, W. Li, K. Yao, and W. Tan, “Toward less hidden cost of code completion with acceptance and ranking models,” in 2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2021, pp. 195–205
2021
Earlier work this paper cites.
D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. Song et al. , “Measuring coding challenge competence with apps,” arXiv preprint arXiv:2105.09938 , 2021
Original
2021
Earlier work this paper cites.
J. Austin, A. Odena, M. Nye, M. Bosma, H. Michalewski, D. Dohan, E. Jiang, C. Cai, M. Terry, Q. Le et al. , “Program synthesis with large language models,” arXiv preprint arXiv:2108.07732 , 2021
Original
2021
Earlier work this paper cites.
H. Wu, Z. Zhang, S. Wang, Y. Lei, B. Lin, Y. Qin, H. Zhang, and X. Mao, “Peculiar: Smart contract vulnerability detection based on crucial data flow graph and pre-training techniques,” in 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2021, pp. 378–389
2021
Earlier work this paper cites.
H. Isotani, H. Washizaki, Y. Fukazawa, T. Nomoto, S. Ouji, and S. Saito, “Duplicate bug report detection by using sentence embedding and fine-tuning,” in 2021 IEEE international conference on software maintenance and evolution (ICSME) . IEEE, 2021, pp. 535–544
2021
Earlier work this paper cites.
L. Ghadhab, I. Jenhani, M. W. Mkaouer, and M. B. Messaoud, “Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model,” Information and Software Technology , vol. 135, p. 106566, 2021
2021
Earlier work this paper cites.
J. Henkel, D. Silva, L. Teixeira, M. d’Amorim, and T. Reps, “Shipwright: A human-in-the-loop system for dockerfile repair,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 1148–1160
2021
Earlier work this paper cites.
Y. Yang, X. Xia, D. Lo, and J. Grundy, “A survey on deep learning for software engineering,” ACM Computing Surveys (CSUR) , vol. 54, no. 10s, pp. 1–73, 2022
2022
Earlier work this paper cites.
C. S. Xia and L. Zhang, “Less training, more repairing please: revisiting automated program repair via zero-shot learning,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 959–971
2022
Earlier work this paper cites.
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, W.-t. Yih, L. Zettlemoyer, and M. Lewis, “Incoder: A generative model for code infilling and synthesis,” arXiv preprint arXiv:2204.05999 , 2022
Original
2022
Earlier work this paper cites.
W. Yuan, Q. Zhang, T. He, C. Fang, N. Q. V. Hung, X. Hao, and H. Yin, “Circle: Continual repair across programming languages,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 678–690
2022
Earlier work this paper cites.
C. Watson, N. Cooper, D. N. Palacio, K. Moran, and D. Poshyvanyk, “A systematic literature review on the use of deep learning in software engineering research,” ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 31, no. 2, pp. 1–58, 2022
2022
Earlier work this paper cites.
S. Wang, L. Huang, A. Gao, J. Ge, T. Zhang, H. Feng, I. Satyarth, M. Li, H. Zhang, and V. Ng, “Machine/deep learning for software engineering: A systematic literature review,” IEEE Transactions on Software Engineering , vol. 49, no. 3, pp. 1188–1231, 2022
2022
Earlier work this paper cites.
C. Niu, C. Li, B. Luo, and V. Ng, “Deep learning meets software engineering: A survey on pre-trained models of source code,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence , 2022, pp. 5546–5555
2022
Earlier work this paper cites.
D. Guo, S. Lu, N. Duan, Y. Wang, M. Zhou, and J. Yin, “Unixcoder: Unified cross-modal pre-training for code representation,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 7212–7225
2022
Earlier work this paper cites.
H. Le, Y. Wang, A. D. Gotmare, S. Savarese, and S. C. H. Hoi, “Coderl: Mastering code generation through pretrained models and deep reinforcement learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 21 314–21 328, 2022
2022
Earlier work this paper cites.
J. Zhang, S. Panthaplackel, P. Nie, J. J. Li, and M. Gligoric, “Coditt5: Pretraining for source code and natural language editing,” in 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
2022
Earlier work this paper cites.
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. Dal Lago et al. , “Competition-level code generation with alphacode,” Science , vol. 378, no. 6624, pp. 1092–1097, 2022
2022
Earlier work this paper cites.
S. Chandel, C. B. Clement, G. Serrato, and N. Sundaresan, “Training and evaluating a jupyter notebook data science assistant,” arXiv preprint arXiv:2201.12901 , 2022
Original
2022
Earlier work this paper cites.
Y. Chai, S. Wang, C. Pang, Y. Sun, H. Tian, and H. Wu, “Ernie-code: Beyond english-centric cross-lingual pretraining for programming languages,” arXiv preprint arXiv:2212.06742 , 2022
Original
2022
Earlier work this paper cites.
F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn, “A systematic evaluation of large language models of code,” in Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming , 2022, pp. 1–10
2022
Earlier work this paper cites.
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “Codegen: An open large language model for code with multi-turn program synthesis,” arXiv preprint arXiv:2203.13474 , 2022
Original
2022
Earlier work this paper cites.
D. Zan, B. Chen, D. Yang, Z. Lin, M. Kim, B. Guan, Y. Wang, W. Chen, and J.-G. Lou, “Cert: Continual pre-training on sketches for library-oriented code generation,” arXiv preprint arXiv:2206.06888 , 2022
Original
2022
Earlier work this paper cites.
F. Christopoulou, G. Lampouras, M. Gritta, G. Zhang, Y. Guo, Z. Li, Q. Zhang, M. Xiao, B. Shen, L. Li et al. , “Pangu-coder: Program synthesis with function-level language modeling,” arXiv preprint arXiv:2207.11280 , 2022
Original
2022
Earlier work this paper cites.
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann et al. , “Palm: Scaling language modeling with pathways,” arXiv preprint arXiv:2204.02311 , 2022
Original
2022
Earlier work this paper cites.
K. Ronanki, B. Cabrero-Daniel, and C. Berger, “Chatgpt as a tool for user story quality evaluation: Trustworthy out of the box?” in International Conference on Agile Software Development . Springer, 2022, pp. 173–181
2022
Earlier work this paper cites.
——, “Identification of intra-domain ambiguity using transformer-based machine learning,” in Proceedings of the 1st International Workshop on Natural Language-based Software Engineering , 2022, pp. 51–58
2022
Earlier work this paper cites.
S. Ezzini, S. Abualhaija, C. Arora, and M. Sabetzadeh, “Automated handling of anaphoric ambiguity in requirements: a multi-solution study,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 187–199
2022
Earlier work this paper cites.
Z. Shi, Y. Xiong, X. Zhang, Y. Zhang, S. Li, and Y. Zhu, “Cross-modal contrastive learning for code search,” in 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2022, pp. 94–105
2022
Earlier work this paper cites.
X. Li, Y. Gong, Y. Shen, X. Qiu, H. Zhang, B. Yao, W. Qi, D. Jiang, W. Chen, and N. Duan, “Coderetriever: A large scale contrastive pre-training method for code search,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022, pp. 2898–2910
2022
Earlier work this paper cites.
P. Salza, C. Schwizer, J. Gu, and H. C. Gall, “On the effectiveness of transfer learning for code search,” IEEE Transactions on Software Engineering , 2022
2022
Earlier work this paper cites.
N. Jain, S. Vaidyanath, A. Iyer, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma, “Jigsaw: Large language models meet program synthesis,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1219–1231
2022
Earlier work this paper cites.
M. Wei, N. S. Harzevili, Y. Huang, J. Wang, and S. Wang, “Clear: contrastive learning for api recommendation,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 376–387
2022
Earlier work this paper cites.
J. Von der Mosel, A. Trautsch, and S. Herbold, “On the validity of pre-trained transformers for natural language processing in the software engineering domain,” IEEE Transactions on Software Engineering , vol. 49, no. 4, pp. 1487–1507, 2022
2022
Earlier work this paper cites.
D. Shen, X. Chen, C. Wang, K. Sen, and D. Song, “Benchmarking language models for code syntax understanding,” arXiv preprint arXiv:2210.14473 , 2022
Original
2022
Earlier work this paper cites.
J. Zhang, S. Liu, L. Gong, H. Zhang, Z. Huang, and H. Jiang, “Beqain: An effective and efficient identifier normalization approach with bert and the question answering system,” IEEE Transactions on Software Engineering , vol. 49, no. 4, pp. 2597–2620, 2022
2022
Earlier work this paper cites.
K. Jesse, P. T. Devanbu, and A. Sawant, “Learning to predict user-defined types,” IEEE Transactions on Software Engineering , vol. 49, no. 4, pp. 1508–1522, 2022
2022
Earlier work this paper cites.
A. Ciborowska and K. Damevski, “Fast changeset-based bug localization with bert,” in Proceedings of the 44th International Conference on Software Engineering (ICSE’22) , 2022, pp. 946–957
2022
Earlier work this paper cites.
M. Tufano, D. Drain, A. Svyatkovskiy, and N. Sundaresan, “Generating accurate assert statements for unit test cases using pretrained transformers,” in Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test , 2022, pp. 54–64
2022
Earlier work this paper cites.
E. Dinella, G. Ryan, T. Mytkowicz, and S. K. Lahiri, “Toga: A neural method for test oracle generation,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2130–2141
2022
Earlier work this paper cites.
Z. Liu, C. Chen, J. Wang, X. Che, Y. Huang, J. Hu, and Q. Wang, “Fill in the blank: Context-aware automated text input generation for mobile gui testing,” arXiv preprint arXiv:2212.04732 , 2022
Original
2022
Earlier work this paper cites.
Z. Sun, J. M. Zhang, Y. Xiong, M. Harman, M. Papadakis, and L. Zhang, “Improving machine translation systems via isotopic replacement,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1181–1192
2022
Earlier work this paper cites.
Z. Liu, Y. Feng, Y. Yin, J. Sun, Z. Chen, and B. Xu, “Qatest: A uniform fuzzing framework for question answering systems,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
2022
Earlier work this paper cites.
Y. Li, S. Wang, and T. N. Nguyen, “Dear: A novel deep learning-based approach for automated program repair,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 511–523
2022
Earlier work this paper cites.
Z. Fan, X. Gao, A. Roychoudhury, and S. H. Tan, “Automated repair of programs from large language models,” arXiv preprint arXiv:2205.10583 , 2022
Original
2022
Earlier work this paper cites.
M. Fu, C. Tantithamthavorn, T. Le, V. Nguyen, and D. Phung, “Vulrepair: a t5-based automated software vulnerability repair,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 935–947
2022
Earlier work this paper cites.
H. Tian, X. Tang, A. Habib, S. Wang, K. Liu, X. Xia, J. Klein, and T. F. Bissyandé, “Is this change the answer to that problem? correlating descriptions of bug and code changes for evaluating patch correctness,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
2022
Earlier work this paper cites.
R. Tufano, S. Masiero, A. Mastropaolo, L. Pascarella, D. Poshyvanyk, and G. Bavota, “Using pre-trained models to boost code review automation,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2291–2302
2022
Earlier work this paper cites.
L. Li, L. Yang, H. Jiang, J. Yan, T. Luo, Z. Hua, G. Liang, and C. Zuo, “Auger: automatically generating review comments with pre-training models,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 1009–1021
2022
Earlier work this paper cites.
Z. Li, S. Lu, D. Guo, N. Duan, S. Jannu, G. Jenks, D. Majumder, J. Green, A. Svyatkovskiy, S. Fu et al. , “Automating code review activities by large-scale pre-training,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 1035–1047
2022
Earlier work this paper cites.
S. Tao, W. Meng, Y. Cheng, Y. Zhu, Y. Liu, C. Du, T. Han, Y. Zhao, X. Wang, and H. Yang, “Logstamp: Automatic online log parsing based on sequence labelling,” ACM SIGMETRICS Performance Evaluation Review , vol. 49, no. 4, pp. 93–98, 2022
2022
Earlier work this paper cites.
J. Lee, K. Han, and H. Yu, “A light bug triage framework for applying large pre-trained language model,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–11
2022
Earlier work this paper cites.
M. Alhamed and T. Storer, “Evaluation of context-aware language models and experts for effort estimation of software maintenance issues,” in 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2022, pp. 129–138
2022
Earlier work this paper cites.
A. Mastropaolo, N. Cooper, D. N. Palacio, S. Scalabrino, D. Poshyvanyk, R. Oliveto, and G. Bavota, “Using transfer learning for code-related tasks,” IEEE Transactions on Software Engineering , vol. 49, no. 4, pp. 1580–1598, 2022
2022
Earlier work this paper cites.
Z. Zeng, H. Tan, H. Zhang, J. Li, Y. Zhang, and L. Zhang, “An extensive study on pre-trained models for program understanding and generation,” in Proceedings of the 31st ACM SIGSOFT international symposium on software testing and analysis , 2022, pp. 39–51
2022
Earlier work this paper cites.
Z. Yang, J. Shi, J. He, and D. Lo, “Natural attack for pre-trained models of code,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 1482–1493
2022
Earlier work this paper cites.
Y. Wan, S. Zhang, H. Zhang, Y. Sui, G. Xu, D. Yao, H. Jin, and L. Sun, “You see what i want you to see: poisoning vulnerabilities in neural code search,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 1233–1245
2022
Earlier work this paper cites.
J. Shi, Z. Yang, B. Xu, H. J. Kang, and D. Lo, “Compressing pre-trained models of code into 3 mb,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–12
2022
Earlier work this paper cites.
M. Fu and C. Tantithamthavorn, “Linevul: A transformer-based line-level vulnerability prediction,” in Proceedings of the 19th International Conference on Mining Software Repositories , 2022, pp. 608–620
2022
Earlier work this paper cites.
X. Luo, Y. Xue, Z. Xing, and J. Sun, “Prcbert: Prompt learning for requirement classification using bert-based pretrained language models,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , 2022, pp. 1–13
2022
Earlier work this paper cites.
J. Y. Khan and G. Uddin, “Automatic detection and analysis of technical debts in peer-review documentation of r packages,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2022, pp. 765–776
2022
Earlier work this paper cites.
M. Izadi, R. Gismondi, and G. Gousios, “Codefill: Multi-token code completion by jointly learning from structure and naming sequences,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 401–412
2022
Earlier work this paper cites.
J.-B. Döderlein, M. Acher, D. E. Khelladi, and B. Combemale, “Piloting copilot and codex: Hot temperature, cold prompts, or black magic?” arXiv preprint arXiv:2210.14699 , 2022
Original
2022
Earlier work this paper cites.
V. Dibia, A. Fourney, G. Bansal, F. Poursabzi-Sangdeh, H. Liu, and S. Amershi, “Aligning offline metrics and human judgments of value of ai-pair programmers,” arXiv preprint arXiv:2210.16494 , 2022
Original
2022
Earlier work this paper cites.
E. Jones and J. Steinhardt, “Capturing failures of large language models via human cognitive biases,” Advances in Neural Information Processing Systems , vol. 35, pp. 11 785–11 799, 2022
2022
Earlier work this paper cites.
P. Bareiß, B. Souza, M. d’Amorim, and M. Pradel, “Code generation tools (almost) for free? a study of few-shot, pre-trained language models on code,” arXiv preprint arXiv:2206.01335 , 2022
Original
2022
Earlier work this paper cites.
M. Fu and C. Tantithamthavorn, “Gpt2sp: A transformer-based agile story point estimation approach,” IEEE Transactions on Software Engineering , vol. 49, no. 2, pp. 611–625, 2022
2022
Earlier work this paper cites.
S. K. Lahiri, S. Fakhoury, A. Naik, G. Sakkas, S. Chakraborty, M. Musuvathi, P. Choudhury, C. von Veh, J. P. Inala, C. Wang et al. , “Interactive code generation via test-driven user-intent formalization,” arXiv preprint arXiv:2208.05950 , 2022
Original
2022
Earlier work this paper cites.
S. Wang, Z. Li, H. Qian, C. Yang, Z. Wang, M. Shang, V. Kumar, S. Tan, B. Ray, P. Bhatia et al. , “Recode: Robustness evaluation of code generation models,” arXiv preprint arXiv:2212.10264 , 2022
Original
2022
Earlier work this paper cites.
H. Su, J. Kasai, C. H. Wu, W. Shi, T. Wang, J. Xin, R. Zhang, M. Ostendorf, L. Zettlemoyer, N. A. Smith et al. , “Selective annotation makes language models better few-shot learners,” arXiv preprint arXiv:2209.01975 , 2022
Original
2022
Earlier work this paper cites.
D. Zan, B. Chen, Z. Lin, B. Guan, Y. Wang, and J.-G. Lou, “When language model meets private library,” arXiv preprint arXiv:2210.17236 , 2022
Original
2022
Earlier work this paper cites.
D. Li, Y. Shen, R. Jin, Y. Mao, K. Wang, and W. Chen, “Generation-augmented query expansion for code retrieval,” arXiv preprint arXiv:2212.10692 , 2022
Original
2022
Earlier work this paper cites.
J. Gu, P. Salza, and H. C. Gall, “Assemble foundation models for automatic code summarization,” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2022, pp. 935–946
2022
Earlier work this paper cites.
F. Chen, F. H. Fard, D. Lo, and T. Bryksin, “On the transferability of pre-trained language models for low-resource programming languages,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 401–412
2022
Earlier work this paper cites.
M. Zhu, K. Suresh, and C. K. Reddy, “Multilingual code snippets training for program translation,” in Proceedings of the AAAI conference on artificial intelligence , vol. 36, no. 10, 2022, pp. 11 783–11 790
2022
Earlier work this paper cites.
I. Abdelaziz, J. Dolby, J. McCusker, and K. Srinivas, “Can machines read coding manuals yet?–a benchmark for building better language models for code understanding,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 4, 2022, pp. 4415–4423
2022
Earlier work this paper cites.
K. Kuznia, S. Mishra, M. Parmar, and C. Baral, “Less is more: Summary of long instructions is better for program synthesis,” arXiv preprint arXiv:2203.08597 , 2022
Original
2022
Earlier work this paper cites.
C. Richter and H. Wehrheim, “Learning realistic mutations: Bug creation for neural bug detectors,” in 2022 IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 2022, pp. 162–173
2022
Earlier work this paper cites.
R. Degiovanni and M. Papadakis, “ μ \mu bert: Mutation testing using pre-trained language models,” in 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) . IEEE, 2022, pp. 160–169
2022
Earlier work this paper cites.
M. Alqarni and A. Azim, “Low level source code vulnerability detection using advanced bert language model,” in Proceedings of the Canadian Conference on Artificial Intelligence-Https://caiac. pubpub. org/pub/gdhb8oq4 (may 27 2022) , 2022
2022
Earlier work this paper cites.
C. Thapa, S. I. Jang, M. E. Ahmed, S. Camtepe, J. Pieprzyk, and S. Nepal, “Transformer-based language models for software vulnerability detection,” in Proceedings of the 38th Annual Computer Security Applications Conference , 2022, pp. 481–496
2022
Earlier work this paper cites.
H. Hanif and S. Maffeis, “Vulberta: Simplified source code pre-training for vulnerability detection,” in 2022 International joint conference on neural networks (IJCNN) . IEEE, 2022, pp. 1–8
2022
Earlier work this paper cites.
H. Wang, W. Qu, G. Katz, W. Zhu, Z. Gao, H. Qiu, J. Zhuge, and C. Zhang, “Jtrans: Jump-aware transformer for binary code similarity detection,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 1–13
2022
Earlier work this paper cites.
S. Ahn, S. Ahn, H. Koo, and Y. Paek, “Practical binary code similarity detection with bert-based transferable similarity learning,” in Proceedings of the 38th Annual Computer Security Applications Conference , 2022, pp. 361–374
2022
Earlier work this paper cites.
Y. Wang, J. Wang, H. Zhang, X. Ming, L. Shi, and Q. Wang, “Where is your app frustrating users?” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2427–2439
2022
Earlier work this paper cites.
R. Sharma, F. Chen, F. Fard, and D. Lo, “An exploratory study on code attention in bert,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 437–448
2022
Earlier work this paper cites.
M. Chochlov, G. A. Ahmed, J. V. Patten, G. Lu, W. Hou, D. Gregg, and J. Buckley, “Using a nearest-neighbour, bert-based approach for scalable clone detection,” in 2022 IEEE International Conference on Software Maintenance and Evolution (ICSME) . IEEE, 2022, pp. 582–591
2022
Earlier work this paper cites.
C. Yang, B. Xu, J. Y. Khan, G. Uddin, D. Han, Z. Yang, and D. Lo, “Aspect-based api review classification: How far can pre-trained transformer model go?” in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2022, pp. 385–395
2022
Earlier work this paper cites.
S. Fatima, T. A. Ghaleb, and L. Briand, “Flakify: A black-box, language model-based predictor for flaky tests,” IEEE Transactions on Software Engineering , vol. 49, no. 4, pp. 1912–1927, 2022
2022
Earlier work this paper cites.
A. Mastropaolo, L. Pascarella, and G. Bavota, “Using deep learning to generate complete log statements,” in Proceedings of the 44th International Conference on Software Engineering , 2022, pp. 2279–2290
2022
Earlier work this paper cites.
J. He, B. Xu, Z. Yang, D. Han, C. Yang, and D. Lo, “Ptm4tag: sharpening tag recommendation of stack overflow posts with pre-trained models,” in Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension , 2022, pp. 1–11
2022
Earlier work this paper cites.
M. Kim, Y. Kim, H. Jeong, J. Heo, S. Kim, H. Chung, and E. Lee, “An empirical study of deep transfer learning-based program repair for kotlin projects,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 1441–1452
2022
Earlier work this paper cites.
C. S. Xia, Y. Wei, and L. Zhang, “Practical program repair in the era of large pre-trained language models,” arXiv preprint arXiv:2210.14179 , 2022
Original
2022
Earlier work this paper cites.
M. Lajkó, V. Csuvik, and L. Vidács, “Towards javascript program repair with generative pre-trained transformer (gpt-2),” in Proceedings of the Third International Workshop on Automated Program Repair , 2022, pp. 61–68
2022
Earlier work this paper cites.
J. Zhang, T. Mytkowicz, M. Kaufman, R. Piskac, and S. K. Lahiri, “Using pre-trained language models to resolve textual and semantic merge conflicts (experience paper),” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 77–88
2022
Earlier work this paper cites.
J. Zhu, G. Xiao, Z. Zheng, and Y. Sui, “Enhancing traceability link recovery with unlabeled data,” in 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2022, pp. 446–457
2022
Earlier work this paper cites.
J. Wang, Y. Huang, C. Chen, Z. Liu, S. Wang, and Q. Wang, “Software testing with large language model: Survey, landscape, and vision,” arXiv preprint arXiv:2307.07221 , 2023
Original
2023
Earlier work this paper cites.
OpenAI, “Chatgpt: Optimizing language models for dialogue,” https://openai.com/blog/chatgpt , 2023
2023
Earlier work this paper cites.
C. S. Xia, Y. Wei, and L. Zhang, “Automated program repair in the era of large pre-trained language models,” in Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery , 2023
2023
Earlier work this paper cites.
Q. Zhang, C. Fang, B. Yu, W. Sun, T. Zhang, and Z. Chen, “Pre-trained model-based automated software vulnerability repair: How far are we?” IEEE Transactions on Dependable and Secure Computing , 2023
2023
Earlier work this paper cites.
Y. Wu, N. Jiang, H. V. Pham, T. Lutellier, J. Davis, L. Tan, P. Babkin, and S. Shah, “How effective are neural networks for fixing security vulnerabilities,” in Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis , ser. ISSTA 2023. Association for Computing Machinery, 2023, pp. 1282–1294
2023
Earlier work this paper cites.
N. Nashid, M. Sintaha, and A. Mesbah, “Retrieval-based prompt selection for code-related few-shot learning,” in Proceedings of the 45th International Conference on Software Engineering (ICSE’23) , 2023
2023
Earlier work this paper cites.
Q. Zhang, C. Fang, T. Zhang, B. Yu, W. Sun, and Z. Chen, “Gamma: Revisiting template-based automated program repair via mask prediction,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 535–547
2023
Earlier work this paper cites.
D. Zan, B. Chen, F. Zhang, D. Lu, B. Wu, B. Guan, W. Yongji, and J.-G. Lou, “Large language models meet nl2code: A survey,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 7443–7464
2023
Earlier work this paper cites.
A. Fan, B. Gokkaya, M. Harman, M. Lyubarskiy, S. Sengupta, S. Yoo, and J. M. Zhang, “Large language models for software engineering: Survey and open problems,” arXiv preprint arXiv:2310.03533 , 2023
Original
2023
Earlier work this paper cites.
X. Hou, Y. Zhao, Y. Liu, Z. Yang, K. Wang, L. Li, X. Luo, D. Lo, J. Grundy, and H. Wang, “Large language models for software engineering: A systematic literature review,” arXiv preprint arXiv:2308.10620 , 2023
Original
2023
Earlier work this paper cites.
Q. Zhang, C. Fang, Y. Ma, W. Sun, and Z. Chen, “A survey of learning-based automated program repair,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 2, pp. 1–69, 2023
2023
Earlier work this paper cites.
M. Mukherjee and V. J. Hellendoorn, “Stack over-flowing with results: the case for domain-specific pre-training over one-size-fits-all models,” arXiv preprint arXiv:2306.03268 , 2023
Original
2023
Earlier work this paper cites.
Y. Wang, H. Le, A. D. Gotmare, N. D. Bui, J. Li, and S. C. Hoi, “Codet5+: Open code large language models for code understanding and generation,” arXiv preprint arXiv:2305.07922 , 2023
Original
2023
Earlier work this paper cites.
P. Shojaee, A. Jain, S. Tipirneni, and C. K. Reddy, “Execution-based code generation using deep reinforcement learning,” arXiv preprint arXiv:2301.13816 , 2023
Original
2023
Earlier work this paper cites.
J. Liu, Y. Zhu, K. Xiao, Q. Fu, X. Han, W. Yang, and D. Ye, “Rltf: Reinforcement learning from unit test feedback,” arXiv preprint arXiv:2307.04349 , 2023
Original
2023
Earlier work this paper cites.
B. Lin, S. Wang, Z. Liu, Y. Liu, X. Xia, and X. Mao, “Cct5: A code-change-oriented pre-trained model,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 1509–1521
2023
Earlier work this paper cites.
Z. Yu, Y. Tao, L. Chen, T. Sun, and H. Yang, “B-coder: Value-based deep reinforcement learning for program synthesis.” CoRR , 2023
2023
Earlier work this paper cites.
L. B. Allal, R. Li, D. Kocetkov, C. Mou, C. Akiki, C. M. Ferrandis, N. Muennighoff, M. Mishra, A. Gu, M. Dey et al. , “Santacoder: don’t reach for the stars!” arXiv preprint arXiv:2301.03988 , 2023
Original
2023
Earlier work this paper cites.
R. Li, L. B. Allal, Y. Zi, N. Muennighoff, D. Kocetkov, C. Mou, M. Marone, C. Akiki, J. Li, J. Chim et al. , “Starcoder: may the source be with you!” arXiv preprint arXiv:2305.06161 , 2023
Original
2023
Earlier work this paper cites.
B. Shen, J. Zhang, T. Chen, D. Zan, B. Geng, A. Fu, M. Zeng, A. Yu, J. Ji, J. Zhao et al. , “Pangu-coder2: Boosting large language models for code with ranking feedback,” arXiv preprint arXiv:2307.14936 , 2023
Original
2023
Earlier work this paper cites.
Q. Zheng, X. Xia, X. Zou, Y. Dong, S. Wang, Y. Xue, Z. Wang, L. Shen, A. Wang, Y. Li et al. , “Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x,” arXiv preprint arXiv:2303.17568 , 2023
Original
2023
Earlier work this paper cites.
E. Nijkamp, H. Hayashi, C. Xiong, S. Savarese, and Y. Zhou, “Codegen2: Lessons for training llms on programming and natural languages,” arXiv preprint arXiv:2305.02309 , 2023
Original
2023
Earlier work this paper cites.
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar et al. , “Llama: Open and efficient foundation language models,” arXiv preprint arXiv:2302.13971 , 2023
Original
2023
Earlier work this paper cites.
T. Le Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, R. Castagné, A. S. Luccioni, F. Yvon, M. Gallé et al. , “Bloom: A 176b-parameter open-access multilingual language model,” 2023
2023
Earlier work this paper cites.
Y. Xu, H. Su, C. Xing, B. Mi, Q. Liu, W. Shi, B. Hui, F. Zhou, Y. Liu, T. Xie et al. , “Lemur: Harmonizing natural language and code for language agents,” arXiv preprint arXiv:2310.06830 , 2023
Original
2023
Earlier work this paper cites.
N. Muennighoff, Q. Liu, A. Zebaze, Q. Zheng, B. Hui, T. Y. Zhuo, S. Singh, X. Tang, L. Von Werra, and S. Longpre, “Octopack: Instruction tuning code large language models,” arXiv preprint arXiv:2308.07124 , 2023
Original
2023
Earlier work this paper cites.
Z. Luo, C. Xu, P. Zhao, Q. Sun, X. Geng, W. Hu, C. Tao, J. Ma, Q. Lin, and D. Jiang, “Wizardcoder: Empowering code large language models with evol-instruct,” arXiv preprint arXiv:2306.08568 , 2023
Original
2023
Earlier work this paper cites.
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong et al. , “A survey of large language models,” arXiv preprint arXiv:2303.18223 , 2023
Original
2023
Earlier work this paper cites.
S. Yin, C. Fu, S. Zhao, K. Li, X. Sun, T. Xu, and E. Chen, “A survey on multimodal large language models,” arXiv preprint arXiv:2306.13549 , 2023
Original
2023
Earlier work this paper cites.
B. Roziere, J. Gehring, F. Gloeckle, S. Sootla, I. Gat, X. E. Tan, Y. Adi, J. Liu, T. Remez, J. Rapin et al. , “Code llama: Open foundation models for code,” arXiv preprint arXiv:2308.12950 , 2023
Original
2023
Earlier work this paper cites.
D. Xie, B. Yoo, N. Jiang, M. Kim, L. Tan, X. Zhang, and J. S. Lee, “Impact of large language models on generating software specifications,” arXiv preprint arXiv:2306.03324 , 2023
Original
2023
Earlier work this paper cites.
S. Mandal, A. Chethan, V. Janfaza, S. Mahmud, T. A. Anderson, J. Turek, J. J. Tithi, and A. Muzahid, “Large language models based automatic synthesis of software specifications,” arXiv preprint arXiv:2304.09181 , 2023
Original
2023
Earlier work this paper cites.
M. A. Khan, M. S. Khan, I. Khan, S. Ahmad, and S. Huda, “Non functional requirements identification and classification using transfer learning model,” IEEE Access , 2023
2023
Earlier work this paper cites.
K. Rahman, A. Ghani, A. Alzahrani, M. U. Tariq, and A. U. Rahman, “Pre-trained model-based nfr classification: Overcoming limited data challenges,” IEEE Access , 2023
2023
Earlier work this paper cites.
L. Han, Q. Zhou, and T. Li, “Improving requirements classification models based on explainable requirements concerns,” in 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW) . IEEE, 2023, pp. 95–101
2023
Earlier work this paper cites.
A. Poudel, J. Lin, and J. Cleland-Huang, “Leveraging transformer-based language models to automate requirements satisfaction assessment,” arXiv preprint arXiv:2312.04463 , 2023
Original
2023
Earlier work this paper cites.
M. R. Hasan, J. Li, I. Ahmed, and H. Bagheri, “Automated repair of declarative software specifications in the era of large language models,” arXiv preprint arXiv:2310.12425 , 2023
Original
2023
Earlier work this paper cites.
A. Moharil and A. Sharma, “Tabasco: A transformer based contextualization toolkit,” Science of Computer Programming , vol. 230, p. 102994, 2023
2023
Earlier work this paper cites.
G. Sridhara, S. Mazumdar et al. , “Chatgpt: A study on its utility for ubiquitous software engineering tasks,” arXiv preprint arXiv:2305.16837 , 2023
Original
2023
Earlier work this paper cites.
K. Kolthoff, C. Bartelt, and S. P. Ponzetto, “Data-driven prototyping via natural-language-based gui retrieval,” Automated software engineering , vol. 30, no. 1, p. 13, 2023
2023
Earlier work this paper cites.
P. Brie, N. Burny, A. Sluÿters, and J. Vanderdonckt, “Evaluating a large language model on searching for gui layouts,” Proceedings of the ACM on Human-Computer Interaction , vol. 7, no. EICS, pp. 1–37, 2023
2023
Earlier work this paper cites.
C. Jain, P. R. Anish, A. Singh, and S. Ghaisas, “A transformer-based approach for abstractive summarization of requirements from obligations in software engineering contracts,” in 2023 IEEE 31st International Requirements Engineering Conference (RE) . IEEE, 2023, pp. 169–179
2023
Earlier work this paper cites.
M. B. Chaaben, L. Burgueño, and H. Sahraoui, “Towards using few-shot prompt learning for automating model completion,” in 2023 IEEE/ACM 45th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) . IEEE, 2023, pp. 7–12
2023
Earlier work this paper cites.
G. Yang, Y. Zhou, X. Chen, X. Zhang, Y. Xu, T. Han, and T. Chen, “A syntax-guided multi-task learning approach for turducken-style code generation,” Empirical Software Engineering , vol. 28, no. 6, p. 141, 2023
2023
Earlier work this paper cites.
S. Zhang, Z. Chen, Y. Shen, M. Ding, J. B. Tenenbaum, and C. Gan, “Planning with large language models for code generation,” arXiv preprint arXiv:2303.05510 , 2023
Original
2023
Earlier work this paper cites.
F. Mu, L. Shi, S. Wang, Z. Yu, B. Zhang, C. Wang, S. Liu, and Q. Wang, “Clarifygpt: Empowering llm-based code generation with intention clarification,” arXiv preprint arXiv:2310.10996 , 2023
Original
2023
Earlier work this paper cites.
A. Ni, S. Iyer, D. Radev, V. Stoyanov, W.-t. Yih, S. Wang, and X. V. Lin, “Lever: Learning to verify language-to-code generation with execution,” in International Conference on Machine Learning . PMLR, 2023, pp. 26 106–26 128
2023
Earlier work this paper cites.
X. Chen, M. Lin, N. Schärli, and D. Zhou, “Teaching large language models to self-debug,” arXiv preprint arXiv:2304.05128 , 2023
Original
2023
Earlier work this paper cites.
A. Chen, J. Scheurer, T. Korbak, J. A. Campos, J. S. Chan, S. R. Bowman, K. Cho, and E. Perez, “Improving code generation by training with natural language feedback,” arXiv preprint arXiv:2303.16749 , 2023
Original
2023
Earlier work this paper cites.
K. Zhang, Z. Li, J. Li, G. Li, and Z. Jin, “Self-edit: Fault-aware code editor for code generation,” arXiv preprint arXiv:2305.04087 , 2023
Original
2023
Earlier work this paper cites.
A. Mastropaolo, L. Pascarella, E. Guglielmi, M. Ciniselli, S. Scalabrino, R. Oliveto, and G. Bavota, “On the robustness of code generation techniques: An empirical study on github copilot,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2149–2160
2023
Earlier work this paper cites.
Y. Feng, S. Vanam, M. Cherukupally, W. Zheng, M. Qiu, and H. Chen, “Investigating code generation performance of chatgpt with crowdsourcing social data,” in 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) . IEEE, 2023, pp. 876–885
2023
Earlier work this paper cites.
D. Yan, Z. Gao, and Z. Liu, “A closer look at different difficulty levels code generation abilities of chatgpt,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1887–1898
2023
Earlier work this paper cites.
C. Liu, X. Bao, H. Zhang, N. Zhang, H. Hu, X. Zhang, and M. Yan, “Improving chatgpt prompt for code generation,” arXiv preprint arXiv:2305.08360 , 2023
Original
2023
Earlier work this paper cites.
F. Cassano, J. Gouwar, D. Nguyen, S. Nguyen, L. Phipps-Costin, D. Pinckney, M.-H. Yee, Y. Zi, C. J. Anderson, M. Q. Feldman et al. , “Multipl-e: a scalable and polyglot approach to benchmarking neural code generation,” IEEE Transactions on Software Engineering , vol. 49, no. 7, pp. 3675–3691, 2023
2023
Earlier work this paper cites.
M. Chen, H. Zhang, C. Wan, Z. Wei, Y. Xu, J. Wang, and X. Gu, “On the effectiveness of large language models in domain-specific code generation,” arXiv preprint arXiv:2312.01639 , 2023
Original
2023
Earlier work this paper cites.
M. Liu, T. Yang, Y. Lou, X. Du, Y. Wang, and X. Peng, “Codegen4libs: A two-stage approach for library-oriented code generation,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 434–445
2023
Earlier work this paper cites.
J. Li, Y. Li, G. Li, Z. Jin, Y. Hao, and X. Hu, “Skcoder: A sketch-based approach for automatic code generation,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2124–2135
2023
Earlier work this paper cites.
D. Huang, Q. Bu, J. M. Zhang, M. Luck, and H. Cui, “Agentcoder: Multi-agent-based code generation with iterative testing and optimisation,” arXiv preprint arXiv:2312.13010 , 2023
Original
2023
Earlier work this paper cites.
J. Li, F. Liu, J. Li, Y. Zhao, G. Li, and Z. Jin, “Mcodesearcher: Multi-view contrastive learning for code search,” in Proceedings of the 14th Asia-Pacific Symposium on Internetware , 2023, pp. 270–280
2023
Earlier work this paper cites.
S. Liu, B. Wu, X. Xie, G. Meng, and Y. Liu, “Contrabert: Enhancing code pre-trained models via contrastive learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 2023, pp. 2476–2487
2023
Earlier work this paper cites.
Z. Shi, Y. Xiong, Y. Zhang, Z. Jiang, J. Zhao, L. Wang, and S. Li, “Improving code search with multi-modal momentum contrastive learning,” in 2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC) . IEEE, 2023, pp. 280–291
2023
Earlier work this paper cites.
E. Shi, Y. Wang, W. Gu, L. Du, H. Zhang, S. Han, D. Zhang, and H. Sun, “Cocosoda: Effective contrastive learning for code search,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2198–2210
2023
Earlier work this paper cites.
B. Wang, R. Li, M. Li, and P. Saxena, “Transmap: Pinpointing mistakes in neural code translation,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 999–1011
2023
Earlier work this paper cites.
M. Jiao, T. Yu, X. Li, G. Qiu, X. Gu, and B. Shen, “On the evaluation of neural code translation: Taxonomy and benchmark,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1529–1541
2023
Earlier work this paper cites.
J. Zhang, P. Nie, J. J. Li, and M. Gligoric, “Multilingual code co-evolution using large language models,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 695–707
2023
Earlier work this paper cites.
D. Wang, B. Chen, S. Li, W. Luo, S. Peng, W. Dong, and X. Liao, “One adapter for all programming languages? adapter tuning for code search and summarization,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 5–16
2023
Earlier work this paper cites.
C. Wang, Y. Lou, J. Liu, and X. Peng, “Generating variable explanations via zero-shot prompt learning,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 748–760
2023
Earlier work this paper cites.
S. A. Rukmono, L. Ochoa, and M. R. Chaudron, “Achieving high-level software component summarization via hierarchical chain-of-thought prompting and static code analysis,” in 2023 IEEE International Conference on Data and Software Engineering (ICoDSE) . IEEE, 2023, pp. 7–12
2023
Earlier work this paper cites.
X. Jin, J. Larson, W. Yang, and Z. Lin, “Binary code summarization: Benchmarking chatgpt/gpt-4 and other large language models,” arXiv preprint arXiv:2312.09601 , 2023
Original
2023
Earlier work this paper cites.
W. Sun, C. Fang, Y. You, Y. Miao, Y. Liu, Y. Li, G. Deng, S. Huang, Y. Chen, Q. Zhang et al. , “Automatic code summarization via chatgpt: How far are we?” arXiv preprint arXiv:2305.12865 , 2023
Original
2023
Earlier work this paper cites.
P. Nie, R. Banerjee, J. J. Li, R. J. Mooney, and M. Gligoric, “Learning deep semantics for test completion,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2111–2123
2023
Earlier work this paper cites.
Z. Li, C. Wang, Z. Liu, H. Wang, D. Chen, S. Wang, and C. Gao, “Cctest: Testing and repairing code completion systems,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1238–1250
2023
Earlier work this paper cites.
T. van Dam, M. Izadi, and A. van Deursen, “Enriching source code with contextual data for code completion models: An empirical study,” arXiv preprint arXiv:2304.12269 , 2023
Original
2023
Earlier work this paper cites.
V. Liventsev, A. Grishina, A. Härmä, and L. Moonen, “Fully autonomous programming with large language models,” arXiv preprint arXiv:2304.10423 , 2023
Original
2023
Earlier work this paper cites.
J. Li, G. Li, Z. Li, Z. Jin, X. Hu, K. Zhang, and Z. Fu, “Codeeditor: Learning to edit source code with pre-trained models,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 6, pp. 1–22, 2023
2023
Earlier work this paper cites.
P. Gupta, A. Khare, Y. Bajpai, S. Chakraborty, S. Gulwani, A. Kanade, A. Radhakrishna, G. Soares, and A. Tiwari, “Grace: Generation using associated code edits,” arXiv preprint arXiv:2305.14129 , 2023
Original
2023
Earlier work this paper cites.
Q. Huang, Y. Wu, Z. Xing, H. Jiang, Y. Cheng, and H. Jin, “Adaptive intellect unleashed: The feasibility of knowledge transfer in large language models,” arXiv preprint arXiv:2308.04788 , 2023
Original
2023
Earlier work this paper cites.
S. Wang, S. Jean, S. Sengupta, J. Gung, N. Pappas, and Y. Zhang, “Measuring and mitigating constraint violations of in-context learning for utterance-to-api semantic parsing,” arXiv preprint arXiv:2305.15338 , 2023
Original
2023
Earlier work this paper cites.
T. Y. Zhuo, X. Du, Z. Xing, J. Sun, H. Quan, L. Li, and L. Zhu, “Pop quiz! do pre-trained code models possess knowledge of correct api names?” arXiv preprint arXiv:2309.07804 , 2023
Original
2023
Earlier work this paper cites.
Q. Huang, Z. Wan, Z. Xing, C. Wang, J. Chen, X. Xu, and Q. Lu, “Let’s chat to find the apis: Connecting human, llm and knowledge graph through ai chain,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 471–483
2023
Earlier work this paper cites.
K. Zhang, H. Zhang, G. Li, J. Li, Z. Li, and Z. Jin, “Toolcoder: Teach code generation models to use api search tools,” arXiv preprint arXiv:2305.04032 , 2023
Original
2023
Earlier work this paper cites.
H. Gilbert, M. Sandborn, D. C. Schmidt, J. Spencer-Smith, and J. White, “Semantic compression with large language models,” arXiv preprint arXiv:2304.12512 , 2023
Original
2023
Earlier work this paper cites.
I. Saberi and F. H. Fard, “Model-agnostic syntactical information for pre-trained programming language models,” in 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR) . IEEE, 2023, pp. 183–193
2023
Earlier work this paper cites.
J. Zhao, Y. Rong, Y. Guo, Y. He, and H. Chen, “Understanding programs by exploiting (fuzzing) test cases,” arXiv preprint arXiv:2305.13592 , 2023
Original
2023
Earlier work this paper cites.
A. Khakhar, S. Mell, and O. Bastani, “Pac prediction sets for large language models of code,” in International Conference on Machine Learning . PMLR, 2023, pp. 16 237–16 249
2023
Earlier work this paper cites.
T. Baral, S. Rahman, B. N. Chanumolu, B. Balcı, T. Tuncer, A. Shi, and W. Lam, “Optimizing continuous development by detecting and preventing unnecessary content generation,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 901–913
2023
Earlier work this paper cites.
Y. Wu, Z. Li, J. M. Zhang, M. Papadakis, M. Harman, and Y. Liu, “Large language models in fault localisation,” arXiv preprint arXiv:2308.15276 , 2023
Original
2023
Earlier work this paper cites.
X. Xu, Z. Zhang, S. Feng, Y. Ye, Z. Su, N. Jiang, S. Cheng, L. Tan, and X. Zhang, “Lmpa: Improving decompilation by synergy of large language model and program analysis,” arXiv preprint arXiv:2306.02546 , 2023
Original
2023
Earlier work this paper cites.
W. K. Wong, H. Wang, Z. Li, Z. Liu, S. Wang, Q. Tang, S. Nie, and S. Wu, “Refining decompiled c code with large language models,” arXiv preprint arXiv:2310.06530 , 2023
Original
2023
Earlier work this paper cites.
N. Jiang, C. Wang, K. Liu, X. Xu, L. Tan, and X. Zhang, “Nova + {}^{\mbox{+}} : Generative language models for binaries,” arXiv preprint arXiv:2311.13721 , 2023
Original
2023
Earlier work this paper cites.
B. Steenhoek, M. M. Rahman, R. Jiles, and W. Le, “An empirical study of deep learning models for vulnerability detection,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2237–2248
2023
Earlier work this paper cites.
C. Zhang, H. Liu, J. Zeng, K. Yang, Y. Li, and H. Li, “Prompt-enhanced software vulnerability detection using chatgpt,” arXiv preprint arXiv:2308.12697 , 2023
Original
2023
Earlier work this paper cites.
D. Noever, “Can large language models find and fix vulnerable software?” arXiv preprint arXiv:2308.10345 , 2023
Original
2023
Earlier work this paper cites.
S. Alagarsamy, C. Tantithamthavorn, and A. Aleti, “A3test: Assertion-augmented automated test case generation,” arXiv preprint arXiv:2302.10352 , 2023
Original
2023
Earlier work this paper cites.
N. Rao, K. Jain, U. Alon, C. Le Goues, and V. J. Hellendoorn, “Cat-lm training language models on aligned code and tests,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 409–420
2023
Earlier work this paper cites.
A. M. Dakhel, A. Nikanjam, V. Majdinasab, F. Khomh, and M. C. Desmarais, “Effective test generation using pre-trained large language models and mutation testing,” arXiv preprint arXiv:2308.16557 , 2023
Original
2023
Earlier work this paper cites.
Z. Xie, Y. Chen, C. Zhi, S. Deng, and J. Yin, “Chatunitest: a chatgpt-based automated unit test generation tool,” arXiv preprint arXiv:2305.04764 , 2023
Original
2023
Earlier work this paper cites.
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,” in International conference on software engineering (ICSE) , 2023
2023
Earlier work this paper cites.
V. Guilherme and A. Vincenzi, “An initial investigation of chatgpt unit test generation capability,” in Proceedings of the 8th Brazilian Symposium on Systematic and Automated Software Testing , 2023, pp. 15–24
2023
Earlier work this paper cites.
Y. Zhang, W. Song, Z. Ji, N. Meng et al. , “How well does llm generate security tests?” arXiv preprint arXiv:2310.00710 , 2023
Original
2023
Earlier work this paper cites.
R. Pan, T. A. Ghaleb, and L. Briand, “Ltm: Scalable and black-box similarity-based test suite minimization based on language models,” arXiv preprint arXiv:2304.01397 , 2023
Original
2023
Earlier work this paper cites.
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang, “Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,” in Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2023) , 2023
2023
Earlier work this paper cites.
Y. Deng, C. S. Xia, C. Yang, S. D. Zhang, S. Yang, and L. Zhang, “Large language models are edge-case fuzzers: Testing deep learning libraries via fuzzgpt,” arXiv preprint arXiv:2304.02014 , 2023
Original
2023
Earlier work this paper cites.
C. Yang, Y. Deng, R. Lu, J. Yao, J. Liu, R. Jabbarvand, and L. Zhang, “White-box compiler fuzzing empowered by large language models,” arXiv preprint arXiv:2310.15991 , 2023
Original
2023
Earlier work this paper cites.
J. Hu, Q. Zhang, and H. Yin, “Augmenting greybox fuzzing with generative ai,” arXiv preprint arXiv:2306.06782 , 2023
Original
2023
Earlier work this paper cites.
A. Dakhama, K. Even-Mendoza, W. B. Langdon, H. D. Menendez, and J. Petke, “Searchgem5: Towards reliable gem5 with search based software testing and large language models,” in 15th Symposium on Search Based Software Engineering (SSBSE): Lecture Notes in Computer Science . Springer, 2023
2023
Earlier work this paper cites.
C. Zhang, M. Bai, Y. Zheng, Y. Li, X. Xie, Y. Li, W. Ma, L. Sun, and Y. Liu, “Understanding large language model based fuzz driver generation,” arXiv preprint arXiv:2307.12469 , 2023
Original
2023
Earlier work this paper cites.
C. Yang, Z. Zhao, and L. Zhang, “Kernelgpt: Enhanced kernel fuzzing via large language models,” arXiv preprint arXiv:2401.00563 , 2023
Original
2023
Earlier work this paper cites.
G. Deng, Y. Liu, V. Mayoral-Vilches, P. Liu, Y. Li, Y. Xu, T. Zhang, Y. Liu, M. Pinzger, and S. Rass, “Pentestgpt: An llm-empowered automatic penetration testing tool,” arXiv preprint arXiv:2308.06782 , 2023
Original
2023
Earlier work this paper cites.
A. Happe and J. Cito, “Getting pwn’d by ai: Penetration testing with large language models,” arXiv preprint arXiv:2308.00121 , 2023
Original
2023
Earlier work this paper cites.
V. Vikram, C. Lemieux, and R. Padhye, “Can large language models write good property-based tests?” arXiv preprint arXiv:2307.04346 , 2023
Original
2023
Earlier work this paper cites.
T.-O. Li, W. Zong, Y. Wang, H. Tian, Y. Wang, and S.-C. Cheung, “Finding failure-inducing test cases with chatgpt,” arXiv preprint arXiv:2304.11686 , 2023
Original
2023
Earlier work this paper cites.
A. Khanfir, R. Degiovanni, M. Papadakis, and Y. L. Traon, “Efficient mutation testing via pre-trained language models,” arXiv preprint arXiv:2301.03543 , 2023
Original
2023
Earlier work this paper cites.
A. R. Ibrahimzada, Y. Chen, R. Rong, and R. Jabbarvand, “Automated bug generation in the era of large language models,” arXiv preprint arXiv:2310.02407 , 2023
Original
2023
Earlier work this paper cites.
Y. Nong, Y. Ou, M. Pradel, F. Chen, and H. Cai, “Vulgen: Realistic vulnerability generation via pattern mining and deep learning,” in IEEE/ACM 45th International Conference on Software Engineering(ICSE) https://www. software-lab. org/publications/icse2023_VulGen. pdf , 2023
2023
Earlier work this paper cites.
Z. Liu, C. Chen, J. Wang, M. Chen, B. Wu, X. Che, D. Wang, and Q. Wang, “Make llm a testing expert: Bringing human-like interaction to mobile gui testing via functionality-aware decisions,” arXiv preprint arXiv:2310.15780 , 2023
Original
2023
Earlier work this paper cites.
B. Yu, Y. Hu, Q. Mang, W. Hu, and P. He, “Automated testing and improvement of named entity recognition systems,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2023. New York, NY, USA: Association for Computing Machinery, 2023, pp. 883–894
2023
Earlier work this paper cites.
W. Wang, J.-t. Huang, W. Wu, J. Zhang, Y. Huang, S. Li, P. He, and M. R. Lyu, “Mttm: Metamorphic testing for textual content moderation software,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2387–2399
2023
Earlier work this paper cites.
H. Li, Y. Hao, Y. Zhai, and Z. Qian, “Assisting static analysis with large language models: A chatgpt experiment,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 2107–2111
2023
Earlier work this paper cites.
Y. Hao, W. Chen, Z. Zhou, and W. Cui, “E&v: Prompting large language models to perform static analysis by pseudo-code execution and verification,” arXiv preprint arXiv:2312.08477 , 2023
Original
2023
Earlier work this paper cites.
M. M. Mohajer, R. Aleithan, N. S. Harzevili, M. Wei, A. B. Belle, H. V. Pham, and S. Wang, “Skipanalyzer: An embodied agent for code analysis with large language models,” arXiv preprint arXiv:2310.18532 , 2023
Original
2023
Earlier work this paper cites.
J. Sun, Z. Xing, Q. Lu, X. Xu, L. Zhu, T. Hoang, and D. Zhao, “Silent vulnerable dependency alert prediction with vulnerability key aspect explanation,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 970–982
2023
Earlier work this paper cites.
A. Silva, S. Fang, and M. Monperrus, “Repairllama: Efficient representations and fine-tuned adapters for program repair,” arXiv preprint arXiv:2312.15698 , 2023
Original
2023
Earlier work this paper cites.
Y. Wei, C. S. Xia, and L. Zhang, “Copiloting the copilots: Fusing large language models with completion engines for automated program repair,” arXiv preprint arXiv:2309.00608 , 2023
Original
2023
Earlier work this paper cites.
K. Huang, X. Meng, J. Zhang, Y. Liu, W. Wang, S. Li, and Y. Zhang, “An empirical study on fine-tuning large language models of code for automated program repair,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1162–1174
2023
Earlier work this paper cites.
N. Jiang, K. Liu, T. Lutellier, and L. Tan, “Impact of code language models on automated program repair,” arXiv preprint arXiv:2302.05020 , 2023
Original
2023
Earlier work this paper cites.
D. Sobania, M. Briesch, C. Hanna, and J. Petke, “An analysis of the automatic bug fixing performance of chatgpt,” arXiv preprint arXiv:2301.08653 , 2023
Original
2023
Earlier work this paper cites.
J. Cao, M. Li, M. Wen, and S.-c. Cheung, “A study on prompt design, advantages and limitations of chatgpt for deep learning program repair,” arXiv preprint arXiv:2304.08191 , 2023
Original
2023
Earlier work this paper cites.
M. Jin, S. Shahriar, M. Tufano, X. Shi, S. Lu, N. Sundaresan, and A. Svyatkovskiy, “Inferfix: End-to-end program repair with llms,” arXiv preprint arXiv:2303.07263 , 2023
Original
2023
Earlier work this paper cites.
H. Joshi, J. C. Sanchez, S. Gulwani, V. Le, G. Verbruggen, and I. Radiček, “Repair is nearly generation: Multilingual program repair with llms,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 5131–5140
2023
Earlier work this paper cites.
C. S. Xia and L. Zhang, “Keep the conversation going: Fixing 162 out of 337 bugs for $0.42 each using chatgpt,” arXiv preprint arXiv:2304.00385 , 2023
Original
2023
Earlier work this paper cites.
E. First, M. N. Rabe, T. Ringer, and Y. Brun, “Baldur: Whole-proof generation and repair with large language models,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 1229–1241
2023
Earlier work this paper cites.
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt, “Examining zero-shot vulnerability repair with large language models,” in 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 2023, pp. 2339–2356
2023
Earlier work this paper cites.
M. C. Tol and B. Sunar, “Zeroleak: Using llms for scalable and cost effective side-channel patching,” arXiv preprint arXiv:2308.13062 , 2023
Original
2023
Earlier work this paper cites.
H. Tian, K. Liu, Y. Li, A. K. Kaboré, A. Koyuncu, A. Habib, L. Li, J. Wen, J. Klein, and T. F. Bissyandé, “The best of both worlds: Combining learned embeddings with engineered features for accurate prediction of correct patches,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 4, pp. 1–34, 2023
2023
Earlier work this paper cites.
T. Le-Cong, D.-M. Luong, X. B. D. Le, D. Lo, N.-H. Tran, B. Quang-Huy, and Q.-T. Huynh, “Invalidator: Automated patch correctness assessment via semantic and syntactic reasoning,” IEEE Transactions on Software Engineering , vol. 49, no. 6, pp. 3411–3429, 2023
2023
Earlier work this paper cites.
X. Zhou, B. Xu, K. Kim, D. Han, T. Le-Cong, J. He, B. Le, and D. Lo, “Patchzero: Zero-shot automatic patch correctness assessment,” arXiv preprint arXiv:2303.00202 , 2023
Original
2023
Earlier work this paper cites.
L. Wang, X. Tang, Y. He, C. Ren, S. Shi, C. Yan, and Z. Li, “Delving into commit-issue correlation to enhance commit message generation models,” arXiv preprint arXiv:2308.00147 , 2023
Original
2023
Earlier work this paper cites.
R. Widyasari, T. Zhang, A. Bouraffa, and D. Lo, “Explaining explanation: An empirical study on explanation in code reviews,” arXiv preprint arXiv:2311.09020 , 2023
Original
2023
Earlier work this paper cites.
Q. Guo, J. Cao, X. Xie, S. Liu, X. Li, B. Chen, and X. Peng, “Exploring the potential of chatgpt in automated code refinement: An empirical study,” arXiv preprint arXiv:2309.08221 , 2023
Original
2023
Earlier work this paper cites.
T. Zhang, I. C. Irsan, F. Thung, and D. Lo, “Cupid: Leveraging chatgpt for more accurate duplicate bug report detection,” arXiv preprint arXiv:2308.10022 , 2023
Original
2023
Earlier work this paper cites.
L. Plein and T. F. Bissyandé, “Can llms demystify bug reports?” arXiv preprint arXiv:2310.06310 , 2023
Original
2023
Earlier work this paper cites.
S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2312–2323
2023
Earlier work this paper cites.
S. Feng and C. Chen, “Prompting is all your need: Automated android bug replay with large language models,” arXiv preprint arXiv:2306.01987 , 2023
Original
2023
Earlier work this paper cites.
X. Hu, Z. Liu, X. Xia, Z. Liu, T. Xu, and X. Yang, “Identify and update test cases when production code changes: A transformer-based approach,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2023, pp. 1111–1122
2023
Earlier work this paper cites.
V.-H. Le and H. Zhang, “Log parsing with prompt-based few-shot learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 2438–2449
2023
Earlier work this paper cites.
Z. Jiang, J. Liu, Z. Chen, Y. Li, J. Huang, Y. Huo, P. He, J. Gu, and M. R. Lyu, “Lilac: Log parsing using llms with adaptive parsing cache,” arXiv preprint arXiv:2310.01796 , 2023
Original
2023
Earlier work this paper cites.
V.-H. Le and H. Zhang, “Log parsing: How far can chatgpt go?” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE Computer Society, 2023, pp. 1699–1704
2023
Earlier work this paper cites.
P. Mudgal and R. Wouhaybi, “An assessment of chatgpt on log data,” in International Conference on AI-generated Content . Springer, 2023, pp. 148–169
2023
Earlier work this paper cites.
S. Dou, J. Shan, H. Jia, W. Deng, Z. Xi, W. He, Y. Wu, T. Gui, Y. Liu, and X. Huang, “Towards understanding the capability of large language models on code clone detection: a survey,” arXiv preprint arXiv:2308.01191 , 2023
Original
2023
Earlier work this paper cites.
A. K. Dipongkor and K. Moran, “A comparative study of transformer-based neural text representation techniques on bug triaging,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 1012–1023
2023
Earlier work this paper cites.
W. Ma, Y. Yu, X. Ruan, and B. Cai, “Pre-trained model based feature envy detection,” in 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR) . IEEE, 2023, pp. 430–440
2023
Earlier work this paper cites.
T. Ahmed, S. Ghosh, C. Bansal, T. Zimmermann, X. Zhang, and S. Rajmohan, “Recommending root-cause and mitigation steps for cloud incidents using large language models,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1737–1749
2023
Earlier work this paper cites.
H. Tu, Z. Zhou, H. Jiang, I. N. B. Yusuf, Y. Li, and L. Jiang, “Isolating compiler bugs by generating effective witness programs with large language models,” arXiv preprint arXiv:2307.00593 , 2023
Original
2023
Earlier work this paper cites.
J. Kannan, “Can llms configure software tools,” arXiv preprint arXiv:2312.06121 , 2023
Original
2023
Earlier work this paper cites.
C. Niu, C. Li, V. Ng, D. Chen, J. Ge, and B. Luo, “An empirical comparison of pre-trained models of source code,” arXiv preprint arXiv:2302.04026 , 2023
Original
2023
Earlier work this paper cites.
S. Gao, X.-C. Wen, C. Gao, W. Wang, H. Zhang, and M. R. Lyu, “What makes good in-context demonstrations for code intelligence tasks with llms?” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 761–773
2023
Earlier work this paper cites.
X. Zhou, K. Kim, B. Xu, J. Liu, D. Han, and D. Lo, “The devil is in the tails: How long-tailed code distributions impact large language models,” arXiv preprint arXiv:2309.03567 , 2023
Original
2023
Earlier work this paper cites.
S. Jalil, S. Rafi, T. D. LaToza, K. Moran, and W. Lam, “Chatgpt and software testing education: Promises & perils,” in 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) . IEEE, 2023, pp. 4130–4137
2023
Earlier work this paper cites.
C. Geng, Z. Yihan, B. Pientka, and X. Si, “Can chatgpt pass an introductory level functional language programming course?” arXiv preprint arXiv:2305.02230 , 2023
Original
2023
Earlier work this paper cites.
P. T. Nguyen, J. Di Rocco, C. Di Sipio, R. Rubei, D. Di Ruscio, and M. Di Penta, “Is this snippet written by chatgpt? an empirical study with a codebert-based classifier,” arXiv preprint arXiv:2307.09381 , 2023
Original
2023
Earlier work this paper cites.
H. Tian, W. Lu, T. O. Li, X. Tang, S.-C. Cheung, J. Klein, and T. F. Bissyandé, “Is chatgpt the ultimate programming assistant–how far is it?” arXiv preprint arXiv:2304.11938 , 2023
Original
2023
Earlier work this paper cites.
Q. Zhang, T. Zhang, J. Zhai, C. Fang, B. Yu, W. Sun, and Z. Chen, “A critical review of large language model on software engineering: An example from chatgpt and automated program repair,” 2023
2023
Earlier work this paper cites.
Y. Wu, Z. Li, J. M. Zhang, and Y. Liu, “Condefects: A new dataset to address the data leakage concern for llm-based fault localization and program repair,” arXiv preprint arXiv:2310.16253 , 2023
Original
2023
Earlier work this paper cites.
J. Liu, C. S. Xia, Y. Wang, and L. Zhang, “Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation,” arXiv preprint arXiv:2305.01210 , 2023
Original
2023
Earlier work this paper cites.
C. Niu, C. Li, V. Ng, and B. Luo, “Crosscodebench: Benchmarking cross-task generalization of source code models,” arXiv preprint arXiv:2302.04030 , 2023
Original
2023
Earlier work this paper cites.
A. Jha and C. K. Reddy, “Codeattack: Code-based adversarial attacks for pre-trained programming language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2023, pp. 14 892–14 900
2023
Earlier work this paper cites.
W. Sun, Y. Chen, G. Tao, C. Fang, X. Zhang, Q. Zhang, and B. Luo, “Backdooring neural code search,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 9692–9708
2023
Earlier work this paper cites.

Similar

J. Li, Z. Li, H. Zhang, G. Li, Z. Jin, X. Hu, and X. Xia, “Poison attack and poison detection on deep source code processing models,” ACM Transactions on Software Engineering and Methodology , 2023
2023
Cited alongside, same era.
Y. Li, S. Liu, K. Chen, X. Xie, T. Zhang, and Y. Liu, “Multi-target backdoor attacks for code pre-trained models,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 . Association for Computational Linguistics, 2023, pp. 7236–7254
2023
Cited alongside, same era.
K. Jesse, T. Ahmed, P. T. Devanbu, and E. Morgan, “Large language models and simple, stupid bugs,” in 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR) . IEEE, 2023, pp. 563–575
2023
Cited alongside, same era.
A. Karmakar and R. Robbes, “Inspect: Intrinsic and systematic probing evaluation for code transformers,” IEEE Transactions on Software Engineering , 2023
2023
Cited alongside, same era.
W. Wang, Y. Wang, S. Joty, and S. C. Hoi, “Rap-gen: Retrieval-augmented patch generation with codet5 for automatic program repair,” arXiv preprint arXiv:2309.06057 , 2023
Original
2023
Cited alongside, same era.
E. Shi, Y. Wang, H. Zhang, L. Du, S. Han, D. Zhang, and H. Sun, “Towards efficient fine-tuning of pre-trained code models: An experimental study and beyond,” in Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis , 2023, pp. 39–51
2023
Cited alongside, same era.
M. Weyssow, X. Zhou, K. Kim, D. Lo, and H. Sahraoui, “Exploring parameter-efficient fine-tuning techniques for code generation with large language models,” arXiv preprint arXiv:2308.10462 , 2023
Original
2023
Cited alongside, same era.
J. Liu, C. Sha, and X. Peng, “An empirical study of parameter-efficient fine-tuning methods for pre-trained code models,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2023, pp. 397–408
2023
Cited alongside, same era.
S. Gao, H. Zhang, C. Gao, and C. Wang, “Keeping pace with ever-increasing data: Towards continual learning of code intelligence models,” arXiv preprint arXiv:2302.03482 , 2023
Original
2023
Cited alongside, same era.
M. Weyssow, X. Zhou, K. Kim, D. Lo, and H. Sahraoui, “On the usage of continual learning for out-of-distribution generalization in pre-trained language models of code,” arXiv preprint arXiv:2305.04106 , 2023
Original
2023
Cited alongside, same era.
P. Jana, P. Jha, H. Ju, G. Kishore, A. Mahajan, and V. Ganesh, “Cotran: An llm-based code translator using reinforcement learning with feedback from compiler and symbolic execution,” arXiv preprint arXiv:2306.06755 , 2023
Original
2023
Cited alongside, same era.
W. Sun, C. Fang, Y. You, Y. Chen, Y. Liu, C. Wang, J. Zhang, Q. Zhang, H. Qian, W. Zhao et al. , “A prompt learning framework for source code summarization,” arXiv preprint arXiv:2312.16066 , 2023
Original
2023
Cited alongside, same era.

Then

M. Zhang, Y. Tian, Z. Xu, Y. Dong, S. H. Tan, and C. Sun, “Lpr: Large language models-aided program reduction,” in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis (Vienna, Austria)(ISSTA 2024). Association for Computing Machinery, New York, NY, USA. https://doi. org/10.1145/3650212.3652126 , 2024
2024
Closest in time.
F. Li, J. Jiang, J. Sun, and H. Zhang, “Hybrid automated program repair by combining large language models and program analysis,” arXiv preprint arXiv:2406.00992 , 2024
Original
2024
Closest in time.
J. Xu, Y. Fu, S. H. Tan, and P. He, “Aligning llms for fl-free program repair,” arXiv preprint arXiv:2404.08877 , 2024
Original
2024
Closest in time.
S. B. Hossain, N. Jiang, Q. Zhou, X. Li, W.-H. Chiang, Y. Lyu, H. Nguyen, and O. Tripp, “A deep dive into large language models for automated bug localization and repair,” arXiv preprint arXiv:2404.11595 , 2024
Original
2024
Closest in time.
B. Yang, H. Tian, J. Ren, H. Zhang, J. Klein, T. F. Bissyandé, C. L. Goues, and S. Jin, “Multi-objective fine-tuning for enhanced program repair with llms,” arXiv preprint arXiv:2404.12636 , 2024
Original
2024
Closest in time.
M. Dehghan, J. J. Wu, F. H. Fard, and A. Ouni, “Mergerepair: An exploratory study on merging task-specific adapters in code llms for automated program repair,” arXiv preprint arXiv:2408.09568 , 2024
Original
2024
Closest in time.
F. V. Ruiz, A. Grishina, M. Hort, and L. Moonen, “A novel approach for automatic program repair using round-trip translation with large language models,” arXiv preprint arXiv:2401.07994 , 2024
Original
2024
Closest in time.
Y. Peng, S. Gao, C. Gao, Y. Huo, and M. R. Lyu, “Domain knowledge matters: Improving prompts with fix templates for repairing python type errors,” in 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE, 2024, pp. 12–24
2024
Closest in time.
J. Xiang, X. Xu, F. Kong, M. Wu, H. Zhang, and Y. Zhang, “How far can we go with practical function-level program repair?” arXiv preprint arXiv:2404.12833 , 2024
Original
2024
Closest in time.
J. Zhao, D. Yang, L. Zhang, X. Lian, and Z. Yang, “Enhancing llm-based automated program repair with design rationales,” arXiv preprint arXiv:2408.12056 , 2024
Original
2024
Closest in time.
N. Wadhwa, J. Pradhan, A. Sonwane, S. P. Sahu, N. Natarajan, A. Kanade, S. Parthasarathy, and S. Rajamani, “Core: Resolving code quality issues using llms,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 789–811, 2024
2024
Closest in time.
B. Yang, H. Tian, W. Pian, H. Yu, H. Wang, J. Klein, T. F. Bissyandé, and S. Jin, “Cref: An llm-based conversational software repair framework for programming tutors,” arXiv preprint arXiv:2406.13972 , 2024
Original
2024
Closest in time.
Y. Charalambous, E. Manino, and L. C. Cordeiro, “Automated repair of ai code with large language models and formal verification,” arXiv preprint arXiv:2405.08848 , 2024
Original
2024
Closest in time.
Q. Xin, H. Wu, S. P. Reiss, and J. Xuan, “Towards practical and useful automated program repair for debugging,” arXiv preprint arXiv:2407.08958 , 2024
Original
2024
Closest in time.
A. Z. Yang, S. Kolak, V. J. Hellendoorn, R. Martins, and C. L. Goues, “Revisiting unnaturalness for automated program repair in the era of large language models,” arXiv preprint arXiv:2404.15236 , 2024
Original
2024
Closest in time.
J. Zhang, J. P. Cambronero, S. Gulwani, V. Le, R. Piskac, G. Soares, and G. Verbruggen, “Pydex: Repairing bugs in introductory python assignments using llms,” Proceedings of the ACM on Programming Languages , vol. 8, no. OOPSLA1, pp. 1100–1124, 2024
2024
Closest in time.
I. Bouzenia, P. Devanbu, and M. Pradel, “Repairagent: An autonomous, llm-based agent for program repair,” arXiv preprint arXiv:2403.17134 , 2024
Original
2024
Closest in time.
C. Lee, C. S. Xia, J.-t. Huang, Z. Zhu, L. Zhang, and M. R. Lyu, “A unified debugging approach via llm-based multi-agent synergy,” arXiv preprint arXiv:2404.17153 , 2024
Original
2024
Closest in time.
X. Yin, C. Ni, S. Wang, Z. Li, L. Zeng, and X. Yang, “Thinkrepair: Self-directed automated program repair,” arXiv preprint arXiv:2407.20898 , 2024
Original
2024
Closest in time.
Q. Xin, H. Wu, J. Tang, X. Liu, S. P. Reiss, and J. Xuan, “Detecting, creating, repairing, and understanding indivisible multi-hunk bugs,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 2747–2770, 2024
2024
Closest in time.
Y. Wang, T. Guo, Z. Huang, and Y. Yuan, “Revisiting evolutionary program repair via code language model,” arXiv preprint arXiv:2408.10486 , 2024
Original
2024
Closest in time.
D. Hidvégi, K. Etemadi, S. Bobadilla, and M. Monperrus, “Cigar: Cost-efficient program repair with llms,” arXiv preprint arXiv:2402.06598 , 2024
Original
2024
Closest in time.
M. Fu, V. Nguyen, C. Tantithamthavorn, D. Phung, and T. Le, “Vision transformer inspired automated vulnerability repair,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 3, pp. 1–29, 2024
2024
Closest in time.
X. Zhou, K. Kim, B. Xu, D. Han, and D. Lo, “Out of sight, out of mind: Better automatic vulnerability repair by broadening input ranges and sources,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Q. Zhang, C. Fang, W. Sun, Y. Liu, T. He, X. Hao, and Z. Chen, “Appt: Boosting automated patch correctness prediction via fine-tuning pre-trained models,” IEEE Transactions on Software Engineering , vol. 50, no. 03, pp. 474–494, 2024
2024
Closest in time.
F. Molina, J. M. Copia, and A. Gorla, “Improving patch correctness analysis via random testing and large language models,” in 2024 IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 2024, pp. 317–328
2024
Closest in time.
J. Li, D. Faragó, C. Petrov, and I. Ahmed, “Only diff is not enough: Generating commit messages leveraging reasoning and action of large language model,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 745–766, 2024
2024
Closest in time.
P. Xue, L. Wu, Z. Yu, Z. Jin, Z. Yang, X. Li, Z. Yang, and Y. Tan, “Automated commit message generation with large language models: An empirical study and beyond,” arXiv preprint arXiv:2404.14824 , 2024
Original
2024
Closest in time.
C. V. Lopes, V. I. Klotzman, I. Ma, and I. Ahmed, “Commit messages in the age of large language models,” arXiv preprint arXiv:2401.17622 , 2024
Original
2024
Closest in time.
R. Tufano, O. Dabić, A. Mastropaolo, M. Ciniselli, and G. Bavota, “Code review automation: strengths and weaknesses of the state of the art,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
Y. Huang, J. Wang, Z. Liu, Y. Wang, S. Wang, C. Chen, Y. Hu, and Q. Wang, “Crashtranslator: Automatically reproducing mobile application crashes directly from stack trace,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
J. Liu, J. Yan, Y. Xie, J. Yan, and J. Zhang, “Augmenting llms to repair obsolete test cases with static collector and neural reranker,” arXiv preprint arXiv:2407.03625 , 2024
Original
2024
Closest in time.
A. S. Yaraghi, D. Holden, N. Kahani, and L. Briand, “Automated test case repair using language models,” arXiv preprint arXiv:2401.06765 , 2024
Original
2024
Closest in time.
Z. Ma, A. R. Chen, D. J. Kim, T.-H. Chen, and S. Wang, “Llmparser: An exploratory study on using large language models for log parsing,” in 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 2024, pp. 1209–1221
2024
Closest in time.
Y. Liu, S. Tao, W. Meng, J. Wang, W. Ma, Y. Chen, Y. Zhao, H. Yang, and Y. Jiang, “Interpretable online log analysis using large language models with prompt strategies,” in Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension , 2024, pp. 35–46
2024
Closest in time.
J. Xu, Z. Cui, Y. Zhao, X. Zhang, S. He, P. He, L. Li, Y. Kang, Q. Lin, Y. Dang et al. , “Unilog: Automatic logging via llm and in-context learning,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
Y. Liu, S. Tao, W. Meng, F. Yao, X. Zhao, and H. Yang, “Logprompt: Prompt engineering towards zero-shot and interpretable log analysis,” in Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings , 2024, pp. 364–365
2024
Closest in time.
Y. Xiao, V.-H. Le, and H. Zhang, “Stronger, faster, and cheaper log parsing with llms,” arXiv preprint arXiv:2406.06156 , 2024
Original
2024
Closest in time.
J. Huang, Z. Jiang, Z. Chen, and M. R. Lyu, “Ulog: Unsupervised log parsing with large language models through log contrastive units,” arXiv preprint arXiv:2406.07174 , 2024
Original
2024
Closest in time.
X. Ma, H. Zou, J. Keung, P. He, Y. Li, X. Yu, and F. Sarro, “On the influence of data resampling for deep learning-based log anomaly detection: Insights and recommendations,” arXiv preprint arXiv:2405.03489 , 2024
Original
2024
Closest in time.
F. Hadadi, Q. Xu, D. Bianculli, and L. Briand, “Anomaly detection on unstable logs with gpt models,” arXiv preprint arXiv:2406.07467 , 2024
Original
2024
Closest in time.
S. Huang, Y. Liu, J. Qi, J. Shang, Z. Xiao, C. Fung, Z. Wu, H. Yang, Z. Luan, and D. Qian, “Gloss: Guiding large language models to answer questions from system logs,” in 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2024, pp. 91–101
2024
Closest in time.
Z. Zhang and T. Saber, “Assessing the code clone detection capability of large language models,” in 2024 4th International Conference on Code Quality (ICCQ) . IEEE, 2024, pp. 75–83
2024
Closest in time.
M. B. Moumoula, A. K. Kabore, J. Klein, and T. Bissyande, “Large language models for cross-language code clone detection,” arXiv preprint arXiv:2408.04430 , 2024
Original
2024
Closest in time.
M. M. Imran, P. Chatterjee, and K. Damevski, “Uncovering the causes of emotions in software developer communication using zero-shot llms,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Y. Cai, A. Yadavally, A. Mishra, G. Montejo, and T. Nguyen, “Programming assistant for exception handling with codebert,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Y. Jiang, C. Zhang, S. He, Z. Yang, M. Ma, S. Qin, Y. Kang, Y. Dang, S. Rajmohan, Q. Lin et al. , “Xpert: Empowering incident management with query recommendations via large language models,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
G. Colavito, F. Lanubile, N. Novielli, and L. Quaranta, “Leveraging gpt-like llms to automate issue labeling,” in 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) . IEEE, 2024, pp. 469–480
2024
Closest in time.
G. Morales, K. Pragyan, S. Jahan, M. B. Hosseini, and R. Slavin, “A large language model approach to code and privacy policy alignment,” in 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2024, pp. 79–90
2024
Closest in time.
N. Mohammed, A. Lal, A. Rastogi, S. Roy, and R. Sharma, “Enabling memory safety of c programs using llms,” arXiv preprint arXiv:2404.01096 , 2024
Original
2024
Closest in time.
Z. Gao, H. Wang, Y. Wang, and C. Zhang, “Vic: Virtual compiler is all you need for assembly code search,” arXiv preprint arXiv:2408.06385 , 2024
Original
2024
Closest in time.
S. J. Oishwee, N. Stakhanova, and Z. Codabux, “Large language model vs. stack overflow in addressing android permission related challenges,” in 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR) . IEEE, 2024, pp. 373–383
2024
Closest in time.
Y. Li, Z. Ren, Z. Wang, L. Yang, L. Dong, C. Zhong, and H. Zhang, “Fine-se: Integrating semantic features and expert features for software effort estimation,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
M. M. Imran, “Emotion classification in software engineering texts: A comparative analysis of pre-trained transformers language models,” in Proceedings of the Third ACM/IEEE International Workshop on NL-based Software Engineering , 2024, pp. 73–80
2024
Closest in time.
S. Abedu, A. Abdellatif, and E. Shihab, “Llm-based chatbots for mining software repositories: Challenges and opportunities,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering , 2024, pp. 201–210
2024
Closest in time.
J. White, S. Hays, Q. Fu, J. Spencer-Smith, and D. C. Schmidt, “Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design,” in Generative AI for Effective Software Development . Springer, 2024, pp. 71–108
2024
Closest in time.
W. Liang and G. Xiao, “An exploratory evaluation of large language models using empirical software engineering tasks,” in Proceedings of the 15th Asia-Pacific Symposium on Internetware , 2024, pp. 31–40
2024
Closest in time.
Y. Xue, H. Chen, G. R. Bai, R. Tairas, and Y. Huang, “Does chatgpt help with introductory programming? an experiment of students using chatgpt in cs1,” in Proceedings of the 46th International Conference on Software Engineering: Software Engineering Education and Training , 2024, pp. 331–341
2024
Closest in time.
Z. Wang, K. Liu, G. Li, and Z. Jin, “Hits: High-coverage llm-based unit test generation via method slicing,” arXiv preprint arXiv:2408.11324 , 2024
Original
2024
Closest in time.
X. Du, M. Liu, K. Wang, H. Wang, J. Liu, Y. Chen, J. Feng, C. Sha, X. Peng, and Y. Lou, “Evaluating large language models in class-level code generation,” in 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 2024, pp. 982–994
2024
Closest in time.
X. Li, K. Dong, Y. Q. Lee, W. Xia, Y. Yin, H. Zhang, Y. Liu, Y. Wang, and R. Tang, “Coir: A comprehensive benchmark for code information retrieval models,” arXiv preprint arXiv:2407.02883 , 2024
Original
2024
Closest in time.
Z. Yang, B. Xu, J. M. Zhang, H. J. Kang, J. Shi, J. He, and D. Lo, “Stealthy backdoor attack for code models,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
Z. Li, C. Wang, P. Ma, C. Liu, S. Wang, D. Wu, C. Gao, and Y. Liu, “On extracting specialized code abilities from large language models: A feasibility study,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
W. Wang, H. Ning, G. Zhang, L. Liu, and Y. Wang, “Rocks coding, not development: A human-centric, experimental evaluation of llm-supported se tasks,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 699–721, 2024
2024
Closest in time.
D. Nam, A. Macvean, V. Hellendoorn, B. Vasilescu, and B. Myers, “Using an llm to help with code understanding,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
R. Serafini, C. Otto, S. A. Horstmann, and A. Naiakshina, “Chatgpt-resistant screening instrument for identifying non-programmers,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
M. H. Tanzil, J. Y. Khan, and G. Uddin, “Chatgpt incorrectness detection in software reviews,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
Y. Li, Y. Liu, G. Deng, Y. Zhang, W. Song, L. Shi, K. Wang, Y. Li, Y. Liu, and H. Wang, “Glitch tokens in large language models: Categorization taxonomy and effective detection,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 2075–2097, 2024
2024
Closest in time.
W. Jiang, J. Zhai, S. Ma, X. Zhang, and C. Shen, “Costello: Contrastive testing for embedding-based large language model as a service embeddings,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 906–928, 2024
2024
Closest in time.
M. Yang, Y. Chen, Y. Liu, and L. Shi, “Distillseq: A framework for safety alignment testing in large language models using knowledge distillation,” arXiv preprint arXiv:2407.10106 , 2024
Original
2024
Closest in time.
S. Hyun, M. Guo, and M. A. Babar, “Metal: Metamorphic testing framework for analyzing large-language model qualities,” in 2024 IEEE Conference on Software Testing, Verification and Validation (ICST) . IEEE, 2024, pp. 117–128
2024
Closest in time.
A. Al-Kaswan, M. Izadi, and A. Van Deursen, “Traces of memorisation in large language models for code,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
Y. Liu, T. Le-Cong, R. Widyasari, C. Tantithamthavorn, L. Li, X.-B. D. Le, and D. Lo, “Refining chatgpt-generated code: Characterizing and mitigating code quality issues,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 5, pp. 1–26, 2024
2024
Closest in time.
Z. Liu, Y. Tang, X. Luo, Y. Zhou, and L. F. Zhang, “No need to lift a finger anymore? assessing the quality of code generation by chatgpt,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
O. J. Idialu, N. S. Mathews, R. Maipradit, J. M. Atlee, and M. Nagappan, “Whodunit: Classifying code as human authored or gpt-4 generated-a case study on codechef problems,” in Proceedings of the 21st International Conference on Mining Software Repositories , 2024, pp. 394–406
2024
Closest in time.
D. Song, X. Xie, J. Song, D. Zhu, Y. Huang, F. Juefei-Xu, and L. Ma, “Luna: A model-based universal analysis framework for large language models,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
D. Yao, J. Zhang, I. G. Harris, and M. Carlsson, “Fuzzllm: A novel and universal fuzzing framework for proactively discovering jailbreak vulnerabilities in large language models,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 4485–4489
2024
Closest in time.
T. Y. Zhuo, A. Zebaze, N. Suppattarachai, L. von Werra, H. de Vries, Q. Liu, and N. Muennighoff, “Astraios: Parameter-efficient instruction tuning code large language models,” arXiv preprint arXiv:2401.00788 , 2024
Original
2024
Closest in time.
S. Gao, W. Mao, C. Gao, L. Li, X. Hu, X. Xia, and M. R. Lyu, “Learning in the wild: Towards leveraging unlabeled data for effectively tuning pre-trained code models,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
A. Z. Yang, H. Tian, H. Ye, R. Martins, and C. L. Goues, “Security vulnerability detection with multitask self-instructed fine-tuning of large language models,” arXiv preprint arXiv:2406.05892 , 2024
Original
2024
Closest in time.
C.-Y. Su and C. McMillan, “Distilled gpt for source code summarization,” Automated Software Engineering , vol. 31, no. 1, p. 22, 2024
2024
Closest in time.
A. E. Gärtner and D. Göhlich, “Automated requirement contradiction detection through formal logic and llms,” Automated Software Engineering , vol. 31, no. 2, p. 49, 2024
2024
Closest in time.
C. Arora, J. Grundy, and M. Abdelrazek, “Advancing requirements engineering through generative ai: Assessing the role of llms,” in Generative AI for Effective Software Development . Springer, 2024, pp. 129–148
2024
Closest in time.
S. Hassani, “Enhancing legal compliance and regulation analysis with large language models,” arXiv preprint arXiv:2404.17522 , 2024
Original
2024
Closest in time.
A. Vogelsang, “From specifications to prompts: On the future of generative llms in requirements engineering,” arXiv preprint arXiv:2408.09127 , 2024
Original
2024
Closest in time.
M. Fazelnia, V. Koscinski, S. Herzog, and M. Mirakhorli, “Lessons from the use of natural language inference (nli) in requirements engineering tasks,” arXiv preprint arXiv:2405.05135 , 2024
Original
2024
Closest in time.
D. Jin, Z. Jin, X. Chen, and C. Wang, “Mare: Multi-agents collaboration framework for requirements engineering,” arXiv preprint arXiv:2405.03256 , 2024
Original
2024
Closest in time.
A. Pilone, P. Meirelles, F. Kon, and W. Maalej, “Multilingual crowd-based requirements engineering using large language models,” arXiv preprint arXiv:2408.06505 , 2024
Original
2024
Closest in time.
K. Ronanki, B. Cabrero-Daniel, J. Horkoff, and C. Berger, “Requirements engineering using generative ai: Prompts and prompting patterns,” in Generative AI for Effective Software Development . Springer, 2024, pp. 109–127
2024
Closest in time.
B. Wang, C. Wang, P. Liang, B. Li, and C. Zeng, “How llms aid in uml modeling: An exploratory study with novice analysts,” arXiv preprint arXiv:2404.17739 , 2024
Original
2024
Closest in time.
C. Tinnes, A. Welter, and S. Apel, “Leveraging large language models for software model completion: Results from industrial and public datasets,” arXiv preprint arXiv:2406.17651 , 2024
Original
2024
Closest in time.
Y. Li, J. Keung, X. Ma, C. Y. Chong, J. Zhang, and Y. Liao, “Llm-based class diagram derivation from user stories with chain-of-thought promptings,” in 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC) . IEEE, 2024, pp. 45–50
2024
Closest in time.
R. Sanyal, B. Ghoshal et al. , “A hybrid approach to extract conceptual diagram from software requirements,” Science of Computer Programming , p. 103186, 2024
2024
Closest in time.
N. Nashid, T. Shabani, P. Alian, and A. Mesbah, “Contextual api completion for unseen repositories using llms,” arXiv preprint arXiv:2405.04600 , 2024
Original
2024
Closest in time.
Y. Ding, Z. Wang, W. Ahmad, H. Ding, M. Tan, N. Jain, M. K. Ramanathan, R. Nallapati, P. Bhatia, D. Roth et al. , “Crosscodeeval: A diverse and multilingual benchmark for cross-file code completion,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
A. Eghbali and M. Pradel, “De-hallucinator: Iterative grounding for llm-based code completion,” arXiv preprint arXiv:2401.01701 , 2024
Original
2024
Closest in time.
L. Gong, S. Wang, M. Elhoushi, and A. Cheung, “Evaluation of llms on syntax-aware code fill-in-the-middle tasks,” arXiv preprint arXiv:2403.04814 , 2024
Original
2024
Closest in time.
L. Zhang, Y. Li, J. Li, X. Xia, J. Yang, R. Luo, M. Wang, L. Chen, J. Liu, and M. Yang, “Hierarchical context pruning: Optimizing real-world code completion with repository-level pretrained code llms,” arXiv preprint arXiv:2406.18294 , 2024
Original
2024
Closest in time.
T. Dinh, J. Zhao, S. Tan, R. Negrinho, L. Lausen, S. Zha, and G. Karypis, “Large language models of code fail at completing code with potential bugs,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
M. Zhang, B. Yuan, H. Li, and K. Xu, “Llm-cloud complete: Leveraging cloud computing for efficient large language model-based code completion,” Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 , vol. 5, no. 1, pp. 295–326, 2024
2024
Closest in time.
H. Tan, Q. Luo, L. Jiang, Z. Zhan, J. Li, H. Zhang, and Y. Zhang, “Prompt-based code completion via multi-retrieval augmented generation,” arXiv preprint arXiv:2405.07530 , 2024
Original
2024
Closest in time.
K. Deng, J. Liu, H. Zhu, C. Liu, J. Li, J. Wang, P. Zhao, C. Zhang, Y. Wu, X. Yin et al. , “R2c2-coder: Enhancing and benchmarking real-world repository-level code completion abilities of code large language models,” arXiv preprint arXiv:2406.01359 , 2024
Original
2024
Closest in time.
Z. Sun, X. Du, F. Song, S. Wang, and L. Li, “When neural code completion models size up the situation: Attaining cheaper and faster completion through dynamic model inference,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
A. Kabir, S. Wang, Y. Tian, M. Asaduzzaman, W. Zhang et al. , “Zs4c: Zero-shot synthesis of compilable code for incomplete code snippets using chatgpt,” arXiv preprint arXiv:2401.14279 , 2024
Original
2024
Closest in time.
M. Dilhara, A. Bellur, T. Bryksin, and D. Dig, “Unprecedented code change automation: The fusion of llms and transformation by example,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 631–653, 2024
2024
Closest in time.
W. Zhang, T. Fu, T. Yuan, G. Zhang, D. Chen, and J. Wang, “A lightweight framework for adaptive retrieval in code completion with critique model,” arXiv preprint arXiv:2406.10263 , 2024
Original
2024
Closest in time.
N. Rao, J. Tsay, K. Kate, V. Hellendoorn, and M. Hirzel, “Ai for low-code for ai,” in Proceedings of the 29th International Conference on Intelligent User Interfaces , 2024, pp. 837–852
2024
Closest in time.
K. Okuda and S. Amarasinghe, “Askit: Unified programming interface for programming with large language models,” in 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) . IEEE, 2024, pp. 41–54
2024
Closest in time.
G. Antal, R. Vozár, and R. Ferenc, “Assessing gpt-4-vision’s capabilities in uml-based code generation,” arXiv preprint arXiv:2404.14370 , 2024
Original
2024
Closest in time.
F. Tambon, A. Nikanjam, F. Khomh, and G. Antoniol, “Assessing programming task difficulty for efficient evaluation of large language models,” arXiv preprint arXiv:2407.21227 , 2024
Original
2024
Closest in time.
N. Alshahwan, M. Harman, I. Harper, A. Marginean, S. Sengupta, and E. Wang, “Assured llm-based software engineering,” arXiv preprint arXiv:2402.04380 , 2024
Original
2024
Closest in time.
D. Ghosh Paul, H. Zhu, and I. Bayley, “Benchmarks and metrics for evaluations of code generation: A critical review,” arXiv e-prints , pp. arXiv–2406, 2024
2024
Closest in time.
A. K. Singh, Y. Yang, K. Tirumala, M. Elhoushi, and A. S. Morcos, “Brevity is the soul of wit: Pruning long files for code generation,” arXiv preprint arXiv:2407.00434 , 2024
Original
2024
Closest in time.
M. Li, A. Mishra, and U. Mujumdar, “Bridging the language gap: Enhancing multilingual prompt-based code generation in llms via zero-shot cross-lingual transfer,” arXiv preprint arXiv:2408.09701 , 2024
Original
2024
Closest in time.
D. Nichols, J. H. Davis, Z. Xie, A. Rajaram, and A. Bhatele, “Can large language models write parallel code?” arXiv preprint arXiv:2401.12554 , 2024
Original
2024
Closest in time.
C. Sun, Y. Sheng, O. Padon, and C. Barrett, “Clover: Clo sed-loop ver ifiable code generation,” in International Symposium on AI Verification . Springer, 2024, pp. 134–155
2024
Closest in time.
T. Ridnik, D. Kredo, and I. Friedman, “Code generation with alphacodium: From prompt engineering to flow engineering,” arXiv preprint arXiv:2401.08500 , 2024
Original
2024
Closest in time.
Y.-D. Tsai, M. Liu, and H. Ren, “Code less, align more: Efficient llm fine-tuning for code generation with data pruning,” arXiv preprint arXiv:2407.05040 , 2024
Original
2024
Closest in time.
J. Chen, X. Hu, Z. Li, C. Gao, X. Xia, and D. Lo, “Code search is all you need? improving code suggestions with code search,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
K. Du, R. Rui, H. Chai, L. Fu, W. Xia, Y. Wang, R. Tang, Y. Yu, and W. Zhang, “Codegrag: Extracting composed syntax graphs for retrieval augmented cross-lingual code generation,” arXiv preprint arXiv:2405.02355 , 2024
Original
2024
Closest in time.
Y. Chen, Y. Liu, F. Meng, Y. Chen, J. Xu, and J. Zhou, “Comments as natural logic pivots: Improve code generation via comment perspective,” arXiv preprint arXiv:2404.07549 , 2024
Original
2024
Closest in time.
J. Li, G. Li, Y. Zhao, Y. Li, Z. Jin, H. Zhu, H. Liu, K. Liu, L. Wang, Z. Fang et al. , “Deveval: Evaluating code generation in practical software projects,” arXiv preprint arXiv:2401.06401 , 2024
Original
2024
Closest in time.
J. Li and R. Mooney, “Distilling algorithmic reasoning from llms via explaining solution programs,” arXiv preprint arXiv:2404.08148 , 2024
Original
2024
Closest in time.
Y. Assogba and D. Ren, “Evaluating long range dependency handling in code generation models using multi-step key retrieval,” arXiv preprint arXiv:2407.21049 , 2024
Original
2024
Closest in time.
D. Wang, J. Zhao, H. Pei, S. Tan, and S. Zha, “Fine-tuning language models for joint rewriting and completion of code with potential bugs,” in Findings of the Association for Computational Linguistics ACL 2024 , 2024, pp. 15 854–15 868
2024
Closest in time.
H. To, M. Nguyen, and N. Bui, “Functional overlap reranking for neural code generation,” in Findings of the Association for Computational Linguistics ACL 2024 , 2024, pp. 3686–3704
2024
Closest in time.
S. Ugare, T. Suresh, H. Kang, S. Misailovic, and G. Singh, “Improving llm code generation with grammar augmentation,” arXiv preprint arXiv:2403.01632 , 2024
Original
2024
Closest in time.
W.-D. Li and K. Ellis, “Is programming by example solved by llms?” arXiv preprint arXiv:2406.08316 , 2024
Original
2024
Closest in time.
H. Koziolek, S. Grüner, R. Hark, V. Ashiwal, S. Linsbauer, and N. Eskandani, “Llm-based and retrieval-augmented control code generation,” in Proc. 1st Int. Workshop on Large Language Models for Coffice (LLM4Code) at ICSE , vol. 2024, 2024
2024
Closest in time.
M. Chen, H. Tian, Z. Liu, X. Ren, and J. Sun, “Jumpcoder: Go beyond autoregressive coder via online modification,” arXiv preprint arXiv:2401.07870 , 2024
Original
2024
Closest in time.
T. Huang, Z. Sun, Z. Jin, G. Li, and C. Lyu, “Knowledge-aware code generation with large language models,” in Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension , 2024, pp. 52–63
2024
Closest in time.
J. Xu, Z. Liu, N. A. V. Suryanarayanan, and H. Iba, “Large language models synergize with automated machine learning,” arXiv preprint arXiv:2405.03727 , 2024
Original
2024
Closest in time.
X. Hu, K. Kuang, J. Sun, H. Yang, and F. Wu, “Leveraging print debugging to improve code generation in large language models,” arXiv preprint arXiv:2401.05319 , 2024
Original
2024
Closest in time.
B. Chen, M. Zhu, B. Dolan-Gavitt, M. Shafique, and S. Garg, “Model cascading for code: Reducing inference costs with model cascading for llm based code generation,” arXiv preprint arXiv:2405.15842 , 2024
Original
2024
Closest in time.
Z. Dai, C. Yao, W. Han, Y. Yuan, Z. Gao, and J. Chen, “Mpcoder: Multi-user personalized code generator with explicit and implicit style representation learning,” arXiv preprint arXiv:2406.17255 , 2024
Original
2024
Closest in time.
S. Wang, L. Ding, L. Shen, Y. Luo, B. Du, and D. Tao, “Oop: Object-oriented programming evaluation benchmark for large language models,” arXiv preprint arXiv:2401.06628 , 2024
Original
2024
Closest in time.
C. Arora, A. I. Sayeed, S. Licorish, F. Wang, and C. Treude, “Optimizing large language model hyperparameters for code generation,” arXiv preprint arXiv:2408.10577 , 2024
Original
2024
Closest in time.
D. Nichols, P. Polasam, H. Menon, A. Marathe, T. Gamblin, and A. Bhatele, “Performance-aligned llms for generating fast code,” arXiv preprint arXiv:2404.18864 , 2024
Original
2024
Closest in time.
M. L. Siddiq, S. Dristi, J. Saha, and J. Santos, “Quality assessment of prompts used in code generation,” arXiv preprint arXiv:2404.10155 , 2024
Original
2024
Closest in time.
M. Riddell, A. Ni, and A. Cohan, “Quantifying contamination in evaluating code generation capabilities of language models,” arXiv preprint arXiv:2403.04811 , 2024
Original
2024
Closest in time.
D. Kang, K. J. Seo, and T. Kim, “Revisiting the impact of pursuing modularity for code generation,” arXiv preprint arXiv:2407.11406 , 2024
Original
2024
Closest in time.
Y. Tian and T. Zhang, “Selective prompt anchoring for code generation,” arXiv preprint arXiv:2408.09121 , 2024
Original
2024
Closest in time.
C. Zhu-Tian, Z. Xiong, X. Yao, and E. Glassman, “Sketch then generate: Providing incremental user feedback and guiding llm code generation through language-oriented code sketches,” arXiv preprint arXiv:2405.03998 , 2024
Original
2024
Closest in time.
L. Sarker, M. Downing, A. Desai, and T. Bultan, “Syntactic robustness for llm-based code generation,” arXiv preprint arXiv:2404.01535 , 2024
Original
2024
Closest in time.
C. Wang, J. Zhang, Y. Feng, T. Li, W. Sun, Y. Liu, and X. Peng, “Teaching code llms to use autocompletion tools in repository-level code generation,” arXiv preprint arXiv:2401.06391 , 2024
Original
2024
Closest in time.
M. Hassid, T. Remez, J. Gehring, R. Schwartz, and Y. Adi, “The larger the better? improved llm code-generation via budget reallocation,” arXiv preprint arXiv:2404.00725 , 2024
Original
2024
Closest in time.
Z.-C. Lyu, X.-Y. Li, Z. Xie, and M. Li, “Top pass: Improve code generation by pass@ k-maximized code ranking,” arXiv preprint arXiv:2408.05715 , 2024
Original
2024
Closest in time.
J. Wu, E. Schoop, A. Leung, T. Barik, J. P. Bigham, and J. Nichols, “Uicoder: Finetuning large language models to generate user interface code through automated feedback,” arXiv preprint arXiv:2406.07739 , 2024
Original
2024
Closest in time.
M. Allamanis, S. Panthaplackel, and P. Yin, “Unsupervised evaluation of code llms with round-trip correctness,” arXiv preprint arXiv:2402.08699 , 2024
Original
2024
Closest in time.
S. Thakur, B. Ahmad, H. Pearce, B. Tan, B. Dolan-Gavitt, R. Karri, and S. Garg, “Verigen: A large language model for verilog code generation,” ACM Transactions on Design Automation of Electronic Systems , vol. 29, no. 3, pp. 1–31, 2024
2024
Closest in time.
L. Guo, Y. Wang, E. Shi, W. Zhong, H. Zhang, J. Chen, R. Zhang, Y. Ma, and Z. Zheng, “When to stop? towards efficient code generation in llms with excess token prevention,” arXiv preprint arXiv:2407.20042 , 2024
Original
2024
Closest in time.
Y. Zhu, J. Li, G. Li, Y. Zhao, Z. Jin, and H. Mei, “Hot or cold? adaptive temperature sampling for code generation with large language models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 1, 2024, pp. 437–445
2024
Closest in time.
A. Varkey, S. Jiang, and W. Huang, “Codecse: A simple multilingual model for code and comment sentence embeddings,” arXiv preprint arXiv:2407.06360 , 2024
Original
2024
Closest in time.
S. Jain, A. Dora, K. S. Sam, and P. Singh, “Llm agents improve semantic code search,” arXiv preprint arXiv:2408.11058 , 2024
Original
2024
Closest in time.
G. Fan, S. Chen, C. Gao, J. Xiao, T. Zhang, and Z. Feng, “Rapid: Zero-shot domain adaptation for code search with pre-trained models,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 5, pp. 1–35, 2024
2024
Closest in time.
H. Li, X. Zhou, and Z. Shen, “Rewriting the code: A simple method for large language model augmented code search,” arXiv preprint arXiv:2401.04514 , 2024
Original
2024
Closest in time.
R. Haldar and J. Hockenmaier, “Analyzing the performance of large language models on code summarization,” arXiv preprint arXiv:2404.08018 , 2024
Original
2024
Closest in time.
J. Zhao, X. Chen, G. Yang, and Y. Shen, “Automatic smart contract comment generation via large language models and in-context learning,” Information and Software Technology , vol. 168, p. 107405, 2024
2024
Closest in time.
J. Kumar and S. Chimalakonda, “Code summarization without direct access to code-towards exploring federated llms for software engineering,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering , 2024, pp. 100–109
2024
Closest in time.
C.-Y. Su, A. Bansal, Y. Huang, T. J.-J. Li, and C. McMillan, “Context-aware code summary generation,” arXiv preprint arXiv:2408.09006 , 2024
Original
2024
Closest in time.
S. Oh and S. Yoo, “Csa-trans: Code structure aware transformer for ast,” arXiv preprint arXiv:2404.05767 , 2024
Original
2024
Closest in time.
B. Poudel, A. Cook, S. Traore, and S. Ameli, “Documint: Docstring generation for python using small language models,” arXiv preprint arXiv:2405.10243 , 2024
Original
2024
Closest in time.
Y. Virk, P. Devanbu, and T. Ahmed, “Enhancing trust in llm-generated code summaries with calibrated confidence scores,” arXiv preprint arXiv:2404.19318 , 2024
Original
2024
Closest in time.
S. Pordanesh and B. Tan, “Exploring the efficacy of large language models (gpt-4) in binary reverse engineering,” arXiv preprint arXiv:2406.06637 , 2024
Original
2024
Closest in time.
S. Kang, L. Milliken, and S. Yoo, “Identifying inaccurate descriptions in llm-generated code comments via test execution,” arXiv preprint arXiv:2406.14836 , 2024
Original
2024
Closest in time.
B. Szalontai, G. Szalay, T. Márton, A. Sike, B. Pintér, and T. Gregorics, “Large language models for code summarization,” 2024. [Online]. Available: https://arxiv.org/abs/2405.19032
Original
2024
Closest in time.
H. Lu, H. Peng, G. Nan, J. Cui, C. Wang, and W. Jin, “Malsight: Exploring malicious source code and benign pseudocode for iterative binary malware summarization,” arXiv preprint arXiv:2406.18379 , 2024
Original
2024
Closest in time.
Y. Wang, X. Li, T. Nguyen, S. Wang, C. Ni, and L. Ding, “Natural is the best: Model-agnostic code simplification for pre-trained large language models,” arXiv preprint arXiv:2405.11196 , 2024
Original
2024
Closest in time.
K. Shi, D. Altınbüken, S. Anand, M. Christodorescu, K. Grünwedel, A. Koenings, S. Naidu, A. Pathak, M. Rasi, F. Ribeiro et al. , “Natural language outlines for code: Literate programming in the llm era,” arXiv preprint arXiv:2408.04820 , 2024
Original
2024
Closest in time.
W. Sun, Y. Miao, Y. Li, H. Zhang, C. Fang, Y. Liu, G. Deng, Y. Liu, and Z. Chen, “Source code summarization in the era of large language models,” arXiv preprint arXiv:2407.07959 , 2024
Original
2024
Closest in time.
Y. Wang, Y. Huang, D. Guo, H. Zhang, and Z. Zheng, “Sparsecoder: Identifier-aware sparse transformer for file-level code summarization,” arXiv preprint arXiv:2401.14727 , 2024
Original
2024
Closest in time.
Y. Shen, X. Ju, X. Chen, and G. Yang, “Bash comment generation via data augmentation and semantic-aware codebert,” Automated Software Engineering , vol. 31, no. 1, p. 30, 2024
2024
Closest in time.
M. Bhattarai, J. E. Santos, S. Jones, A. Biswas, B. Alexandrov, and D. O’Malley, “Enhancing code translation in language models with few-shot learning via retrieval-augmented generation,” arXiv preprint arXiv:2407.19619 , 2024
Original
2024
Closest in time.
M. Macedo, Y. Tian, F. Cogo, and B. Adams, “Exploring the impact of the output format on the evaluation of large language models for code translation,” in Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering , 2024, pp. 57–68
2024
Closest in time.
M. T. Dearing, Y. Tao, X. Wu, Z. Lan, and V. Taylor, “Lassi: An llm-based automated self-correcting pipeline for translating parallel scientific codes,” arXiv preprint arXiv:2407.01638 , 2024
Original
2024
Closest in time.
X. Yin, C. Ni, T. N. Nguyen, S. Wang, and X. Yang, “Rectifier: Code translation with corrector via llms,” arXiv preprint arXiv:2407.07472 , 2024
Original
2024
Closest in time.
V. Nitin and B. Ray, “Spectra: Enhancing the code translation ability of language models by generating multi-modal specifications,” arXiv preprint arXiv:2405.18574 , 2024
Original
2024
Closest in time.
H. F. Eniser, H. Zhang, C. David, M. Wang, B. Paulsen, J. Dodds, and D. Kroening, “Towards translating real-world code with llms: A study of translating to rust,” arXiv preprint arXiv:2405.11514 , 2024
Original
2024
Closest in time.
A. Z. Yang, Y. Takashima, B. Paulsen, J. Dodds, and D. Kroening, “Vert: Verified equivalent rust transpilation with few-shot learning,” arXiv preprint arXiv:2404.18852 , 2024
Original
2024
Closest in time.
X. Li, S. Yuan, X. Gu, Y. Chen, and B. Shen, “Few-shot code translation via task-adapted prompt learning,” Journal of Systems and Software , vol. 212, p. 112002, 2024
2024
Closest in time.
Y. Ding, J. Peng, M. J. Min, G. Kaiser, J. Yang, and B. Ray, “Semcoder: Training code language models with comprehensive semantics,” arXiv preprint arXiv:2406.01006 , 2024
Original
2024
Closest in time.
F. Artuso, M. Mormando, G. A. Di Luna, and L. Querzoni, “Binbert: Binary code understanding with a fine-tunable and execution-aware transformer,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
Closest in time.
N. Tao, A. Ventresque, V. Nallur, and T. Saber, “Enhancing program synthesis with large language models using many-objective grammar-guided genetic programming,” Algorithms , vol. 17, no. 7, p. 287, 2024
2024
Closest in time.
Y. Li, J. Parsert, and E. Polgreen, “Guiding enumerative program synthesis with large language models,” in International Conference on Computer Aided Verification . Springer, 2024, pp. 280–301
2024
Closest in time.
S. Barke, E. A. Gonzalez, S. R. Kasibatla, T. Berg-Kirkpatrick, and N. Polikarpova, “Hysynth: Context-free llm approximation for guiding program synthesis,” arXiv preprint arXiv:2405.15880 , 2024
Original
2024
Closest in time.
S. Yang, X. Chen, K. Liu, G. Yang, and C. Yu, “Automatic bi-modal question title generation for stack overflow with prompt learning,” Empirical Software Engineering , vol. 29, no. 3, p. 63, 2024
2024
Closest in time.
X. Zong, S. Zheng, H. Zou, H. Yu, and S. Gao, “Graphpyrec: A novel graph-based approach for fine-grained python code recommendation,” Science of Computer Programming , p. 103166, 2024
2024
Closest in time.
S. Kabir, D. N. Udo-Imeh, B. Kou, and T. Zhang, “Is stack overflow obsolete? an empirical study of the characteristics of chatgpt answers to stack overflow questions,” in Proceedings of the CHI Conference on Human Factors in Computing Systems , 2024, pp. 1–17
2024
Closest in time.
E. Firouzi and M. Ghafari, “Time to separate from stackoverflow and match with chatgpt for encryption,” Journal of Systems and Software , p. 112135, 2024
2024
Closest in time.
A. Abdellatif, K. Badran, D. E. Costa, and E. Shihab, “A transformer-based approach for augmenting software engineering chatbots datasets,” arXiv preprint arXiv:2407.11955 , 2024
Original
2024
Closest in time.
L. Zhang, S. Lu, and N. Duan, “Selene: Pioneering automated proof in software verification,” arXiv preprint arXiv:2401.07663 , 2024
Original
2024
Closest in time.
B. Cheng, C. Zhang, K. Wang, L. Shi, Y. Liu, H. Wang, Y. Guo, and X. Chen, “Semantic-enhanced indirect call analysis with large language models,” arXiv preprint arXiv:2408.04344 , 2024
Original
2024
Closest in time.
M. Hassan, S. Ahmadi-Pour, K. Qayyum, C. K. Jha, and R. Drechsler, “Llm-guided formal verification coupled with mutation testing,” in 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) . IEEE, 2024, pp. 1–2
2024
Closest in time.
F. Tip, J. Bell, and M. Schäfer, “Llmorpheus: Mutation testing using large language models,” arXiv preprint arXiv:2404.09952 , 2024
Original
2024
Closest in time.
Z. Li and D. Shin, “Mutation-based consistency testing for evaluating the code understanding capability of llms,” in Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering-Software Engineering for AI , 2024, pp. 150–159
2024
Closest in time.
B. Steenhoek, M. M. Rahman, M. K. Roy, M. S. Alam, E. T. Barr, and W. Le, “A comprehensive study of the capabilities of large language models for vulnerability detection,” arXiv preprint arXiv:2403.17218 , 2024
Original
2024
Closest in time.
M. M. Kholoosi, M. A. Babar, and R. Croft, “A qualitative study on using chatgpt for software security: Perception vs. practicality,” arXiv preprint arXiv:2408.00435 , 2024
Original
2024
Closest in time.
B. Ju, J. Yang, T. Yu, T. Abdullayev, Y. Wu, D. Wang, and Y. Zhao, “A study of using multimodal llms for non-crash functional bug detection in android apps,” arXiv preprint arXiv:2407.19053 , 2024
Original
2024
Closest in time.
M. Saad, J. A. H. López, B. Chen, D. Varró, and T. Sharma, “Alpine: An adaptive language-agnostic pruning method for language models for code,” arXiv preprint arXiv:2407.04147 , 2024
Original
2024
Closest in time.
E. Pelofske, V. Urias, and L. M. Liebrock, “Automated software vulnerability static code analysis using generative pre-trained transformer models,” arXiv preprint arXiv:2408.00197 , 2024
Original
2024
Closest in time.
Y. Chen, C. Gao, Z. Yang, H. Zhang, and Q. Liao, “Bridge and hint: Extending pre-trained language models for long-range code,” arXiv preprint arXiv:2405.11233 , 2024
Original
2024
Closest in time.
S. Wan, J. Saxe, C. Gomes, S. Chennabasappa, A. Rath, K. Sun, and X. Wang, “Bridging the gap: A study of ai-based vulnerability management between industry and academia,” arXiv preprint arXiv:2405.02435 , 2024
Original
2024
Closest in time.
H. Lee, S. Sharma, and B. Hu, “Bug in the code stack: Can llms find bugs in large python code stacks,” arXiv preprint arXiv:2406.15325 , 2024
Original
2024
Closest in time.
Y. Nong, M. Aldeen, L. Cheng, H. Hu, F. Chen, and H. Cai, “Chain-of-thought prompting of large language models for discovering and fixing software vulnerabilities,” arXiv preprint arXiv:2402.17230 , 2024
Original
2024
Closest in time.
Z. Wang, G. Li, J. Li, Y. Dong, Y. Xiong, and Z. Jin, “Code structure-aware through line-level semantic learning for code vulnerability detection,” arXiv preprint arXiv:2407.18877 , 2024
Original
2024
Closest in time.
Y. Zhao, L. Gong, Z. Huang, Y. Wang, M. Wei, and F. Wu, “Coding-ptms: How to find optimal code pre-trained models for code embedding in vulnerability detection?” arXiv preprint arXiv:2408.04863 , 2024
Original
2024
Closest in time.
X. Zhou, D.-M. Tran, T. Le-Cong, T. Zhang, I. C. Irsan, J. Sumarlin, B. Le, and D. Lo, “Comparison of static application security testing tools and large language models for repo-level vulnerability detection,” arXiv preprint arXiv:2407.16235 , 2024
Original
2024
Closest in time.
B. Steenhoek, H. Gao, and W. Le, “Dataflow analysis-inspired deep learning for efficient vulnerability detection,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Y. Yang, X. Zhou, R. Mao, J. Xu, L. Yang, Y. Zhangm, H. Shen, and H. Zhang, “Dlap: A deep learning augmented large language model prompting framework for software vulnerability detection,” arXiv preprint arXiv:2405.01202 , 2024
Original
2024
Closest in time.
K. Lucas, R. Gheyi, E. Soares, M. Ribeiro, and I. Machado, “Evaluating large language models in detecting test smells,” arXiv preprint arXiv:2407.19261 , 2024
Original
2024
Closest in time.
S. S. Daneshvar, Y. Nong, X. Yang, S. Wang, and H. Cai, “Exploring rag-based vulnerability augmentation with llms,” arXiv preprint arXiv:2408.04125 , 2024
Original
2024
Closest in time.
A. Shestov, A. Cheshkov, R. Levichev, R. Mussabayev, P. Zadorozhny, E. Maslov, C. Vadim, and E. Bulychev, “Finetuning large language models for vulnerability detection,” arXiv preprint arXiv:2401.17010 , 2024
Original
2024
Closest in time.
G. Lu, X. Ju, X. Chen, W. Pei, and Z. Cai, “Grace: Empowering llm-based software vulnerability detection with graph structure and in-context learning,” Journal of Systems and Software , vol. 212, p. 112031, 2024
2024
Closest in time.
K. Tamberg and H. Bahsi, “Harnessing large language models for software vulnerability detection: A comprehensive benchmarking study,” arXiv preprint arXiv:2405.15614 , 2024
Original
2024
Closest in time.
A. A. Mahyari, “Harnessing the power of llms in source code vulnerability detection,” arXiv preprint arXiv:2408.03489 , 2024
Original
2024
Closest in time.
X. Zhou, S. Cao, X. Sun, and D. Lo, “Large language model for vulnerability detection and repair: Literature review and roadmap,” arXiv preprint arXiv:2404.02525 , 2024
Original
2024
Closest in time.
K. Dozono, T. E. Gasiba, and A. Stocco, “Large language models for secure code assessment: A multi-language empirical study,” arXiv preprint arXiv:2408.06428 , 2024
Original
2024
Closest in time.
N. S. Mathews, Y. Brus, Y. Aafer, M. Nagappan, and S. McIntosh, “Llbezpeky: Leveraging large language models for vulnerability detection,” arXiv preprint arXiv:2401.01269 , 2024
Original
2024
Closest in time.
V. Ashiwal, S. Finster, and A. Dawoud, “Llm-based vulnerability sourcing from unstructured data,” in 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) . IEEE, 2024, pp. 634–641
2024
Closest in time.
Y. Cheng, L. K. Shar, T. Zhang, S. Yang, C. Dong, D. Lo, S. Lv, Z. Shi, and L. Sun, “Llm-enhanced static analysis for precise identification of vulnerable oss versions,” arXiv preprint arXiv:2408.07321 , 2024
Original
2024
Closest in time.
Y. Sun, D. Wu, Y. Xue, H. Liu, W. Ma, L. Zhang, M. Shi, and Y. Liu, “Llm4vuln: A unified evaluation framework for decoupling and enhancing llms’ vulnerability reasoning,” arXiv preprint arXiv:2401.16185 , 2024
Original
2024
Closest in time.
Z. Wang, G. Li, J. Li, Y. Xiong, and Z. Jin, “M2cvd: Multi-model collaboration for code vulnerability detection,” arXiv preprint arXiv:2406.05940 , 2024
Original
2024
Closest in time.
Z. Mao, J. Li, M. Li, and K. Tei, “Multi-role consensus through llms discussions for vulnerability detection,” arXiv preprint arXiv:2403.14274 , 2024
Original
2024
Closest in time.
X. Yin and C. Ni, “Multitask-based evaluation of open-source llm on software vulnerability,” arXiv preprint arXiv:2404.02056 , 2024
Original
2024
Closest in time.
C. X. Do, N. T. Luu, and P. T. L. Nguyen, “Optimizing software vulnerability detection using roberta and machine learning,” Automated Software Engineering , vol. 31, no. 2, p. 40, 2024
2024
Closest in time.
X. Yin, “Pros and cons! evaluating chatgpt on software vulnerability,” arXiv preprint arXiv:2404.03994 , 2024
Original
2024
Closest in time.
J. Gonçalves, T. Dias, E. Maia, and I. Praça, “Scope: Evaluating llms for software vulnerability detection,” arXiv preprint arXiv:2407.14372 , 2024
Original
2024
Closest in time.
J. Yu, P. Liang, Y. Fu, A. Tahir, M. Shahin, C. Wang, and Y. Cai, “Security code review by llms: A deep dive into responses,” arXiv preprint arXiv:2401.16310 , 2024
Original
2024
Closest in time.
Q. Mao, Z. Li, X. Hu, K. Liu, X. Xia, and J. Sun, “Towards effectively detecting and explaining vulnerabilities using large language models,” arXiv preprint arXiv:2406.09701 , 2024
Original
2024
Closest in time.
S. M. Taghavi and F. Feyzi, “Using large language models to better detect and handle software vulnerabilities and cyber security threats,” 2024
2024
Closest in time.
X. Du, G. Zheng, K. Wang, J. Feng, W. Deng, M. Liu, B. Chen, X. Peng, T. Ma, and Y. Lou, “Vul-rag: Enhancing llm-based vulnerability detection via knowledge-level rag,” arXiv preprint arXiv:2406.11147 , 2024
Original
2024
Closest in time.
A. H. A. Chukkol, S. Luo, K. Sharif, Y. Haruna, and M. M. Abdullahi, “Vulcatch: Enhancing binary vulnerability detection through codet5 decompilation and kan advanced feature extraction,” arXiv preprint arXiv:2408.07181 , 2024
Original
2024
Closest in time.
Y. Liu, L. Gao, M. Yang, Y. Xie, P. Chen, X. Zhang, and W. Chen, “Vuldetectbench: Evaluating the deep capability of vulnerability detection with large language models,” arXiv preprint arXiv:2406.07595 , 2024
Original
2024
Closest in time.
X.-C. Wen, X. Wang, Y. Chen, R. Hu, D. Lo, and C. Gao, “Vuleval: Towards repository-level evaluation of software vulnerability detection,” arXiv preprint arXiv:2404.15596 , 2024
Original
2024
Closest in time.
Y. Ding, Y. Fu, O. Ibrahim, C. Sitawarin, X. Chen, B. Alomair, D. Wagner, B. Ray, and Y. Chen, “Vulnerability detection with code language models: How far are we?” arXiv preprint arXiv:2403.18624 , 2024
Original
2024
Closest in time.
Z. Jiang, W. Sun, X. Gu, J. Wu, T. Wen, H. Hu, and M. Yan, “Dfept: Data flow embedding for enhancing pre-trained model based vulnerability detection,” in Proceedings of the 15th Asia-Pacific Symposium on Internetware , 2024, pp. 95–104
2024
Closest in time.
D. Pratama, N. Suryanto, A. A. Adiputra, T.-T.-H. Le, A. Y. Kadiptya, M. Iqbal, and H. Kim, “Cipher: Cybersecurity intelligent penetration-testing helper for ethical researcher,” arXiv preprint arXiv:2408.11650 , 2024
Original
2024
Closest in time.
J. Su, L. Deng, C. Wen, S. Qin, and C. Tian, “cfstra: Enhancing configurable program analysis through llm-driven strategy selection based on code features,” in International Symposium on Theoretical Aspects of Software Engineering . Springer, 2024, pp. 374–391
2024
Closest in time.
S. Yan, S. Wang, Y. Duan, H. Hong, K. Lee, D. Kim, and Y. Hong, “An llm-assisted easy-to-trigger backdoor attack on code completion models: Injecting disguised vulnerabilities against strong detection,” arXiv preprint arXiv:2406.06822 , 2024
Original
2024
Closest in time.
Y. Xia, Z. Xie, P. Liu, K. Lu, Y. Liu, W. Wang, and S. Ji, “Exploring automatic cryptographic api misuse detection in the era of llms,” arXiv preprint arXiv:2407.16576 , 2024
Original
2024
Closest in time.
F. He, J. Zhai, and M. Pan, “Beyond code generation: Assessing code llm maturity with postconditions,” arXiv preprint arXiv:2407.14118 , 2024
Original
2024
Closest in time.
H. Wang, H. Hu, C. Chen, and B. Turhan, “Chat-like asserts prediction with the support of large language model,” arXiv preprint arXiv:2407.21429 , 2024
Original
2024
Closest in time.
B. Mali, K. Maddala, S. Reddy, V. Gupta, C. Karfa, and R. Karri, “Chiraag: Chatgpt informed rapid and automated assertion generation,” arXiv preprint arXiv:2402.00093 , 2024
Original
2024
Closest in time.
Y. Feng, H. Li, Y. Cao, Y. Wang, and H. Feng, “Crabs-former: Cross-architecture binary code similarity detection based on transformer,” in Proceedings of the 15th Asia-Pacific Symposium on Internetware , 2024, pp. 11–20
2024
Closest in time.
Y. Qin, S. Wang, Y. Lou, J. Dong, K. Wang, X. Li, and X. Mao, “Agentfl: Scaling llm-based fault localization to project-level context,” arXiv preprint arXiv:2403.16362 , 2024
Original
2024
Closest in time.
J. Yan, J. Huang, C. Fang, J. Yan, and J. Zhang, “Better debugging: Combining static analysis and llms for explainable crashing fault localization,” arXiv preprint arXiv:2408.12070 , 2024
Original
2024
Closest in time.
S. Shan, Y. Huo, Y. Su, Y. Li, D. Li, and Z. Zheng, “Face it yourselves: An llm-based two-stage strategy to localize configuration errors via logs,” arXiv preprint arXiv:2404.00640 , 2024
Original
2024
Closest in time.
S. Ji, S. Lee, C. Lee, H. Im, and Y.-S. Han, “Impact of large language models of code on fault localization,” arXiv preprint arXiv:2408.09657 , 2024
Original
2024
Closest in time.
S. Bin Murtaza, A. Mccoy, Z. Ren, A. Murphy, and W. Banzhaf, “Llm fault localisation within evolutionary computation based automated program repair,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion , 2024, pp. 1824–1829
2024
Closest in time.
Y. Wu, Z. Li, J. M. Zhang, and Y. Liu, “Condefects: A complementary dataset to address the data leakage concern for llm-based fault localization and program repair,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering , 2024, pp. 642–646
2024
Closest in time.
Y. Liu, Y. Xue, D. Wu, Y. Sun, Y. Li, M. Shi, and Y. Liu, “Propertygpt: Llm-driven formal verification of smart contracts through retrieval-augmented property generation,” arXiv preprint arXiv:2405.02580 , 2024
Original
2024
Closest in time.
C. Wen, J. Cao, J. Su, Z. Xu, S. Qin, M. He, H. Li, S.-C. Cheung, and C. Tian, “Enchanting program specification synthesis by large language models using static analysis and program verification,” in International Conference on Computer Aided Verification . Springer, 2024, pp. 302–328
2024
Closest in time.
P. J. Chapman, C. Rubio-González, and A. V. Thakur, “Interleaving static analysis and llm prompting,” in Proceedings of the 13th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis , 2024, pp. 9–17
2024
Closest in time.
C. Fang, N. Miao, S. Srivastav, J. Liu, R. Zhang, R. Fang, R. Tsang, N. Nazari, H. Wang, H. Homayoun et al. , “Large language models for code analysis: Do { \{ LLMs } \} really do their job?” in 33rd USENIX Security Symposium (USENIX Security 24) , 2024, pp. 829–846
2024
Closest in time.
C. Wen, Y. Cai, B. Zhang, J. Su, Z. Xu, D. Liu, S. Qin, Z. Ming, and T. Cong, “Automatically inspecting thousands of static bug warnings with large language model: How far are we?” ACM Transactions on Knowledge Discovery from Data , vol. 18, no. 7, pp. 1–34, 2024
2024
Closest in time.
A. Lops, F. Narducci, A. Ragone, M. Trizio, and C. Bartolini, “A system for automated unit test generation using large language models and assessment of generated test suites,” arXiv preprint arXiv:2408.07846 , 2024
Original
2024
Closest in time.
X. Yuan, J. Li, D. Wang, Y. Chen, X. Mao, L. Huang, H. Xue, W. Wang, K. Ren, and J. Wang, “S-eval: Automatic and adaptive test generation for benchmarking safety evaluation of large language models,” arXiv preprint arXiv:2405.14191 , 2024
Original
2024
Closest in time.
Z. Zhou, Y. Tang, Y. Lin, and J. He, “An llm-based readability measurement for unit tests’ context-aware inputs,” arXiv preprint arXiv:2407.21369 , 2024
Original
2024
Closest in time.
L. Plein, W. C. Ouédraogo, J. Klein, and T. F. Bissyandé, “Automatic generation of test cases based on bug reports: a feasibility study with large language models,” in Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings , 2024, pp. 360–361
2024
Closest in time.
H. Kirinuki and H. Tanno, “Chatgpt and human synergy in black-box testing: A comparative analysis,” arXiv preprint arXiv:2401.13924 , 2024
Original
2024
Closest in time.
A. Azaria, R. Azoulay, and S. Reches, “Chatgpt is a remarkable tool—for experts,” Data Intelligence , vol. 6, no. 1, pp. 240–296, 2024
2024
Closest in time.
N. Mündler, M. N. Müller, J. He, and M. Vechev, “Code agents are state of the art software testers,” arXiv preprint arXiv:2406.12952 , 2024
Original
2024
Closest in time.
M. Li, D. Li, J. Liu, J. Cao, Y. Tian, and S.-C. Cheung, “Dllens: Testing deep learning libraries via llm-aided synthesis,” arXiv preprint arXiv:2406.07944 , 2024
Original
2024
Closest in time.
C. Yang, J. Chen, B. Lin, J. Zhou, and Z. Wang, “Enhancing llm-based test generation for hard-to-cover branches via program analysis,” arXiv preprint arXiv:2404.04966 , 2024
Original
2024
Closest in time.
Y. He, J. Wang, Y. Rong, and H. Chen, “Exploring fuzzing as data augmentation for neural test generation,” arXiv preprint arXiv:2406.08665 , 2024
Original
2024
Closest in time.
C. Arora, T. Herda, and V. Homm, “Generating test scenarios from nl requirements using retrieval-augmented llms: An industrial study,” arXiv preprint arXiv:2404.12772 , 2024
Original
2024
Closest in time.
R. Karanjai, A. Hussain, M. R. I. Rabin, L. Xu, W. Shi, and M. A. Alipour, “Harnessing the power of llms: Automating unit test generation for high-performance computing,” arXiv preprint arXiv:2407.05202 , 2024
Original
2024
Closest in time.
K. Li and Y. Yuan, “Large language models as test case generators: Performance evaluation and enhancement,” arXiv preprint arXiv:2404.13340 , 2024
Original
2024
Closest in time.
T. Li, C. Cui, L. Ma, D. Towey, Y. Xie, and R. Huang, “Leveraging large language models for automated web-form-test generation: An empirical study,” arXiv preprint arXiv:2405.09965 , 2024
Original
2024
Closest in time.
K. Etemadi, B. Mohammadi, Z. Su, and M. Monperrus, “Mokav: Execution-driven differential testing with llms,” arXiv preprint arXiv:2406.10375 , 2024
Original
2024
Closest in time.
M. Nabeel, D. D. Nimara, and T. Zanouda, “Test code generation for telecom software systems using two-stage generative model,” arXiv preprint arXiv:2404.09249 , 2024
Original
2024
Closest in time.
Q. Motger, A. Miaschi, F. Dell’Orletta, X. Franch, and J. Marco, “T-frex: A transformer-based feature extraction method from mobile app reviews,” in 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2024, pp. 227–238
2024
Closest in time.
X. Wu, H. Li, N. Yoshioka, H. Washizaki, and F. Khomh, “Refining gpt-3 embeddings with a siamese structure for technical post duplicate detection,” in 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . IEEE, 2024, pp. 114–125
2024
Closest in time.
N. T. Islam, J. Khoury, A. Seong, G. D. L. T. Parra, E. Bou-Harb, and P. Najafirad, “Llm-powered code vulnerability repair with reinforcement learning and semantic reward,” arXiv preprint arXiv:2401.03374 , 2024
Original
2024
Closest in time.
I. Saberi, F. Fard, and F. Chen, “Utilization of pre-trained language models for adapter-based knowledge transfer in software engineering,” Empirical Software Engineering , vol. 29, no. 4, p. 94, 2024
2024
Closest in time.
Y. Du, T. Ma, L. Wu, X. Zhang, and S. Ji, “Adaccd: Adaptive semantic contrasts discovery based cross lingual adaptation for code clone detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 16, 2024, pp. 17 942–17 950
2024
Closest in time.
G. Colavito, F. Lanubile, N. Novielli, and L. Quaranta, “Impact of data quality for automatic issue classification using pre-trained language models,” Journal of Systems and Software , vol. 210, p. 111838, 2024
2024
Closest in time.
L. Dong-Kyu, “A gpt-based code review system for programming language learning,” arXiv preprint arXiv:2407.04722 , 2024
Original
2024
Closest in time.
M. Vijayvergiya, M. Salawa, I. Budiselić, D. Zheng, P. Lamblin, M. Ivanković, J. Carin, M. Lewko, J. Andonov, G. Petrović et al. , “Ai-assisted assessment of coding practices in modern code review,” arXiv preprint arXiv:2405.13565 , 2024
Original
2024
Closest in time.
Z. Rasheed, M. A. Sami, M. Waseem, K.-K. Kemell, X. Wang, A. Nguyen, K. Systä, and P. Abrahamsson, “Ai-powered code review with llms: Early results,” arXiv preprint arXiv:2404.18496 , 2024
Original
2024
Closest in time.
T. Ahmed, P. Devanbu, C. Treude, and M. Pradel, “Can llms replace manual annotation of software engineering artifacts?” arXiv preprint arXiv:2408.05534 , 2024
Original
2024
Closest in time.
C. Koutcheme, N. Dainese, A. Hellas, S. Sarsa, J. Leinonen, S. Ashraf, and P. Denny, “Evaluating language models for generating and judging programming feedback,” arXiv preprint arXiv:2407.04873 , 2024
Original
2024
Closest in time.
L. Fan, J. Liu, Z. Liu, D. Lo, X. Xia, and S. Li, “Exploring the capabilities of llms for code change related tasks,” arXiv preprint arXiv:2407.02824 , 2024
Original
2024
Closest in time.
C. Pornprasit and C. Tantithamthavorn, “Fine-tuning and prompt engineering for large language models-based code review automation,” Information and Software Technology , vol. 175, p. 107523, 2024
2024
Closest in time.
O. Ben Sghaier and H. Sahraoui, “Improving the learning of code review successive tasks with cross-task knowledge distillation,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 1086–1106, 2024
2024
Closest in time.
I. Ferreira, A. Rafiq, and J. Cheng, “Incivility detection in open source code review and issue discussions,” Journal of Systems and Software , vol. 209, p. 111935, 2024
2024
Closest in time.
N. McAleese, R. M. Pokorny, J. F. C. Uribe, E. Nitishinskaya, M. Trebacz, and J. Leike, “Llm critics help catch llm bugs,” arXiv preprint arXiv:2407.00215 , 2024
Original
2024
Closest in time.
Z. Zhang, Z. Xing, X. Ren, Q. Lu, and X. Xu, “Refactoring to pythonic idioms: A hybrid knowledge-driven approach leveraging large language models,” Proceedings of the ACM on Software Engineering , vol. 1, no. FSE, pp. 1107–1128, 2024
2024
Closest in time.
Y. Zhang, Z. Qiu, K.-J. Stol, W. Zhu, J. Zhu, Y. Tian, and H. Liu, “Automatic commit message generation: A critical review and directions for future work,” IEEE Transactions on Software Engineering , 2024
2024
Closest in time.
T. Ye, T. Ma, L. Wu, X. Zhang, S. Ji, and W. Wang, “Iterative or innovative? a problem-oriented perspective for code optimization,” arXiv preprint arXiv:2406.11935 , 2024
Original
2024
Closest in time.
C. Cummins, V. Seeker, D. Grubisic, B. Roziere, J. Gehring, G. Synnaeve, and H. Leather, “Meta large language model compiler: Foundation models of compiler optimization,” arXiv preprint arXiv:2407.02524 , 2024
Original
2024
Closest in time.
D. Grubisic, V. Seeker, G. Synnaeve, H. Leather, J. Mellor-Crummey, and C. Cummins, “Priority sampling of large language models for compilers,” in Proceedings of the 4th Workshop on Machine Learning and Systems , 2024, pp. 91–97
2024
Closest in time.
M. Romero Rosas, M. Torres Sanchez, and R. Eigenmann, “Should ai optimize your code? a comparative study of current large language models versus classical optimizing compilers,” arXiv e-prints , pp. arXiv–2406, 2024
2024
Closest in time.
L. Ma, W. Yang, B. Xu, S. Jiang, B. Fei, J. Liang, M. Zhou, and Y. Xiao, “Knowlog: Knowledge enhanced pre-trained language model for log understanding,” in Proceedings of the 46th IEEE/ACM International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Y. Wu, S. Yu, and Y. Li, “Log parsing with self-generated in-context learning and self-correction,” arXiv preprint arXiv:2406.03376 , 2024
Original
2024
Closest in time.
A. Mastropaolo, V. Ferrari, L. Pascarella, and G. Bavota, “Log statements generation via deep learning: Widening the support provided to developers,” Journal of Systems and Software , vol. 210, p. 111947, 2024
2024
Closest in time.
Z. Liu, C. Chen, J. Wang, M. Chen, B. Wu, Z. Tian, Y. Huang, J. Hu, and Q. Wang, “Testing the limits: Unusual text inputs generation for mobile app crash detection with large language model,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–12
2024
Closest in time.
M. Shafikuzzaman, M. R. Islam, A. C. Rolli, S. Akhter, and N. Seliya, “An empirical evaluation of the zero-shot, few-shot, and traditional fine-tuning based pretrained language models for sentiment analysis in software engineering,” IEEE Access , 2024
2024
Closest in time.
N. T. Islam and P. Najafirad, “Code security vulnerability repair using reinforcement learning with large language models,” arXiv preprint arXiv:2401.07031 , 2024
Original
2024
Closest in time.
Z. Sun, H. Zhu, B. Xu, X. Du, L. Li, and D. Lo, “Llm as runtime error handler: A promising pathway to adaptive self-healing of software systems,” arXiv preprint arXiv:2408.01055 , 2024
Original
2024
Closest in time.
Y. Liu, C. Tantithamthavorn, Y. Liu, and L. Li, “On the reliability and explainability of language models for program generation,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 5, pp. 1–26, 2024
2024
Closest in time.
Y. W. Chow, L. Di Grazia, and M. Pradel, “Pyty: Repairing static type errors in python,” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13
2024
Closest in time.
Y. Zhao, Z. Huang, Y. Ma, R. Li, K. Zhang, H. Jiang, Q. Liu, L. Zhu, and Y. Su, “Repair: Automated program repair with process-based feedback,” arXiv preprint arXiv:2408.11296 , 2024
Original
2024
Closest in time.
R. Gharibi, M. H. Sadreddini, and S. M. Fakhrahmad, “T5apr: Empowering automated program repair across languages through checkpoint ensemble,” Journal of Systems and Software , vol. 214, p. 112083, 2024
2024
Closest in time.
A. Ali, Y. Xia, Q. Umer, and M. Osman, “Bert based severity prediction of bug reports for the maintenance of mobile applications,” Journal of Systems and Software , vol. 208, p. 111898, 2024
2024
Closest in time.
J. Acharya and G. Ginde, “Graph neural network vs. large language model: A comparative analysis for bug report priority and severity prediction,” in Proceedings of the 20th International Conference on Predictive Models and Data Analytics in Software Engineering , 2024, pp. 2–11
2024
Closest in time.
U. Kulsum, H. Zhu, B. Xu, and M. d’Amorim, “A case study of llm for automated vulnerability repair: Assessing impact of reasoning and patch validation feedback,” arXiv preprint arXiv:2405.15690 , 2024
Original
2024
Closest in time.
R. Wang, Z. Li, C. Wang, Y. Xiao, and C. Gao, “Navrepair: Node-type aware c/c++ code vulnerability repair,” arXiv preprint arXiv:2405.04994 , 2024
Original
2024
Closest in time.
Z. Ságodi, G. Antal, B. Bogenfürst, M. Isztin, P. Hegedűs, and R. Ferenc, “Reality check: Assessing gpt-4 in fixing real-world software vulnerabilities,” in Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering , 2024, pp. 252–261
2024
Closest in time.

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