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Automated program repair has emerged as a powerful technique to mitigate the impact of software bugs on system reliability and user experience.
A. J. Ko, B. A. Myers, M. J. Coblenz, and H. H. Aung, “An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks,” IEEE Transactions on software engineering , vol. 32, no. 12, pp. 971–987, 2006
2006
Earlier work this paper cites.
A. Zeller, Why programs fail: a guide to systematic debugging . Elsevier, 2009
2009
Earlier work this paper cites.
C. Le Goues, T. Nguyen, S. Forrest, and W. Weimer, “Genprog: A generic method for automatic software repair,” IEEE Trans. Software Eng. , vol. 38, no. 1, pp. 54–72, 2012
2012
Earlier work this paper cites.
J. Campos, A. Riboira, A. Perez, and R. Abreu, “Gzoltar: an eclipse plug-in for testing and debugging,” in ASE , 2012, pp. 378–381
2012
Earlier work this paper cites.
D. Kim, J. Nam, J. Song, and S. Kim, “Automatic patch generation learned from human-written patches.” in International Conference on Software Engineering (ICSE) , 2013, pp. 802–811
2013
Earlier work this paper cites.
H. D. T. Nguyen, D. Qi, A. Roychoudhury, and S. Chandra, “Semfix: program repair via semantic analysis,” in 35th International Conference on Software Engineering, ICSE ’13, San Francisco, CA, USA, May 18-26, 2013 , 2013, pp. 772–781
2013
Earlier work this paper cites.
R. Just, D. Jalali, and M. D. Ernst, “Defects4j: a database of existing faults to enable controlled testing studies for java programs,” in ISSTA , 2014, pp. 437–440
2014
Earlier work this paper cites.
Y. Ke, K. T. Stolee, C. Le Goues, and Y. Brun, “Repairing programs with semantic code search (t),” in ASE . IEEE, 2015, pp. 295–306
2015
Earlier work this paper cites.
J. Xuan, M. Martinez, F. Demarco, M. Clement, S. L. Marcote, T. Durieux, D. Le Berre, and M. Monperrus, “Nopol: Automatic repair of conditional statement bugs in java programs,” IEEE Transactions on Software Engineering , vol. 43, no. 1, pp. 34–55, 2016
2016
Earlier work this paper cites.
S. Mechtaev, J. Yi, and A. Roychoudhury, “Angelix: Scalable multiline program patch synthesis via symbolic analysis,” in Proceedings of the 38th international conference on software engineering , 2016, pp. 691–701
2016
Earlier work this paper cites.
F. Long and M. Rinard, “Automatic patch generation by learning correct code,” in POPL , 2016, pp. 298–312
2016
Earlier work this paper cites.
X. D. Le, D. Lo, and C. Le Goues, “History driven program repair,” in SANER , 2016, pp. 213–224. [Online]. Available: https://doi.org/10.1109/SANER.2016.76
2016
Earlier work this paper cites.
R. Gupta, S. Pal, A. Kanade, and S. K. Shevade, “Deepfix: Fixing common C language errors by deep learning,” in AAAI , 2017, pp. 1345–1351. [Online]. Available: http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14603
2017
Earlier work this paper cites.
M. Böhme, E. O. Soremekun, S. Chattopadhyay, E. Ugherughe, and A. Zeller, “Where is the bug and how is it fixed? an experiment with practitioners,” in ESEC/FSE , 2017, pp. 117–128
2017
Earlier work this paper cites.
T. Yu and M. Pradel, “Pinpointing and repairing performance bottlenecks in concurrent programs,” Empirical Software Engineering (EMSE) , pp. 1–38, 2017
2017
Earlier work this paper cites.
R. van Tonder and C. L. Goues, “Static automated program repair for heap properties,” in ICSE , 2018, pp. 151–162. [Online]. Available: https://doi.org/10.1145/3180155.3180250
2018
Earlier work this paper cites.
K. Wang, R. Singh, and Z. Su, “Search, align, and repair: data-driven feedback generation for introductory programming exercises,” in PLDI , 2018, pp. 481–495
2018
Earlier work this paper cites.
J. Harer, O. Ozdemir, T. Lazovich, C. P. Reale, R. L. Russell, L. Y. Kim, and S. P. Chin, “Learning to repair software vulnerabilities with generative adversarial networks,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada. , 2018, pp. 7944–7954. [Online]. Available: http://papers.nips.cc/paper/8018-learning-to-repair-software-vulnerabilities-with-generative-adversarial-networks
2018
Earlier work this paper cites.
C. Le Goues, M. Pradel, and A. Roychoudhury, “Automated program repair,” Commun. ACM , vol. 62, no. 12, pp. 56–65, 2019. [Online]. Available: https://doi.org/10.1145/3318162
2019
Earlier work this paper cites.
K. Liu, A. Koyuncu, D. Kim, and T. F. Bissyandé, “Tbar: revisiting template-based automated program repair,” in ISSTA . ACM, 2019, pp. 31–42. [Online]. Available: https://doi.org/10.1145/3293882.3330577
2019
Earlier work this paper cites.
J. Bader, A. Scott, M. Pradel, and S. Chandra, “Getafix: Learning to fix bugs automatically,” Proc. ACM Program. Lang. , vol. 3, no. OOPSLA, pp. 159:1–159:27, 2019. [Online]. Available: https://doi.org/10.1145/3360585
2019
Earlier work this paper cites.
R. Bavishi, H. Yoshida, and M. R. Prasad, “Phoenix: automated data-driven synthesis of repairs for static analysis violations,” in ESEC/FSE , 2019, pp. 613–624. [Online]. Available: https://doi.org/10.1145/3338906.3338952
2019
Earlier work this paper cites.
M. Tufano, J. Pantiuchina, C. Watson, G. Bavota, and D. Poshyvanyk, “On learning meaningful code changes via neural machine translation,” in ICSE , 2019, pp. 25–36. [Online]. Available: https://dl.acm.org/citation.cfm?id=3339509
2019
Earlier work this paper cites.
Z. Chen, S. Kommrusch, M. Tufano, L. Pouchet, D. Poshyvanyk, and M. Monperrus, “SequenceR: Sequence-to-sequence learning for end-to-end program repair,” IEEE Trans. Software Eng. , vol. 47, no. 9, pp. 1943–1959, 2021. [Online]. Available: https://doi.org/10.1109/TSE.2019.2940179
2019
Earlier work this paper cites.
A. Marginean, J. Bader, S. Chandra, M. Harman, Y. Jia, K. Mao, A. Mols, and A. Scott, “Sapfix: Automated end-to-end repair at scale,” in ICSE-SEIP , 2019
2019
Cited alongside, same era.
R. Gupta, A. Kanade, and S. K. Shevade, “Deep reinforcement learning for syntactic error repair in student programs,” in The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019 . AAAI Press, 2019, pp. 930–937. [Online]. Available: https://doi.org/10.1609/aaai.v33i01.3301930
2019
Cited alongside, same era.
M. Vasic, A. Kanade, P. Maniatis, D. Bieber, and R. Singh, “Neural program repair by jointly learning to localize and repair,” in ICLR , 2019
2019
Cited alongside, same era.
L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J.-R. Wen, “A survey on large language model based autonomous agents,” 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
L. D. Grazia and M. Pradel, “Code search: A survey of techniques for finding code,” ACM Comput. Surv. , vol. 55, no. 11, pp. 220:1–220:31, 2023. [Online]. Available: https://doi.org/10.1145/3565971
2023
Later among the works it cites.
Y. Liu, S. Mechtaev, P. Subotić, and A. Roychoudhury, “Program repair guided by datalog-defined static analysis,” in ESEC/FSE , 2023, pp. 1216–1228
2023
Later among the works it cites.
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2020
Cited alongside, same era.
Y. Li, S. Wang, and T. N. Nguyen, “Dlfix: Context-based code transformation learning for automated program repair,” in ICSE , 2020
2020
Cited alongside, same era.
R.-M. Karampatsis and C. Sutton, “How often do single-statement bugs occur?” Jun. 2020. [Online]. Available: http://dx.doi.org/10.1145/3379597.3387491
2020
Cited alongside, same era.
D. Tarlow, S. Moitra, A. Rice, Z. Chen, P. Manzagol, C. Sutton, and E. Aftandilian, “Learning to fix build errors with graph2diff neural networks,” in ICSE ’20: 42nd International Conference on Software Engineering, Workshops, Seoul, Republic of Korea, 27 June - 19 July, 2020 . ACM, 2020, pp. 19–20. [Online]. Available: https://doi.org/10.1145/3387940.3392181
2020
Cited alongside, same era.
Q. Zhu, Z. Sun, Y. Xiao, W. Zhang, K. Yuan, Y. Xiong, and L. Zhang, “A syntax-guided edit decoder for neural program repair,” in ESEC/FSE . ACM, 2021, pp. 341–353. [Online]. Available: https://doi.org/10.1145/3468264.3468544
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Zhang, J. Zhai, S. Ma, and C. Shen, “AUTOTRAINER: an automatic DNN training problem detection and repair system,” in 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021 . IEEE, 2021, pp. 359–371. [Online]. Available: https://doi.org/10.1109/ICSE43902.2021.00043
2021
Cited alongside, same era.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
Cited alongside, same era.
H. Ye, M. Martinez, X. Luo, T. Zhang, and M. Monperrus, “Selfapr: Self-supervised program repair with test execution diagnostics,” in ASE , 2022, pp. 92:1–92:13. [Online]. Available: https://doi.org/10.1145/3551349.3556926
2022
Cited alongside, same era.
N. Jain, S. Gandhi, A. Sonwane, A. Kanade, N. Natarajan, S. Parthasarathy, S. Rajamani, and R. Sharma, “Staticfixer: From static analysis to static repair,” 2023
2023
Later among the works it 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
Later among the works it cites.
H. Joshi, J. P. C. Sánchez, S. Gulwani, V. Le, G. Verbruggen, and I. Radicek, “Repair is nearly generation: Multilingual program repair with llms,” in Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023 , B. Williams, Y. Chen, and J. Neville, Eds. AAAI Press, 2023, pp. 5131–5140. [Online]. Available: https://doi.org/10.1609/aaai.v37i4.25642
2023
Later among the works it cites.
D. Shrivastava, H. Larochelle, and D. Tarlow, “Repository-level prompt generation for large language models of code,” in International Conference on Machine Learning . PMLR, 2023, pp. 31 693–31 715
2023
Later among the works it 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 45th International Conference on Software Engineering, ser. ICSE , 2023
2023
Later among the works it cites.
M. Schäfer, S. Nadi, A. Eghbali, and F. Tip, “An empirical evaluation of using large language models for automated unit test generation,” IEEE Trans. Software Eng. , vol. 50, no. 1, pp. 85–105, 2024. [Online]. Available: https://doi.org/10.1109/TSE.2023.3334955
2023
Later among the works it cites.
S. Kang, J. Yoon, and S. Yoo, “Large language models are few-shot testers: Exploring llm-based general bug reproduction,” in 45th IEEE/ACM International Conference on Software Engineering, ICSE , 2023, pp. 2312–2323. [Online]. Available: https://doi.org/10.1109/ICSE48619.2023.00194
2023
Later among the works it cites.
R. Bairi, A. Sonwane, A. Kanade, V. D. C, A. Iyer, S. Parthasarathy, S. Rajamani, B. Ashok, and S. Shet, “Codeplan: Repository-level coding using llms and planning,” 2023
2023
Later among the works it cites.
H. Ye and M. Monperrus, “Iter: Iterative neural repair for multi-location patches,” in ICSE , 2024
2024
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2024
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Y. W. Chow, L. D. Grazia, and M. Pradel, “Pyty: Repairing static type errors in python,” in International Conference on Software Engineering (ICSE) , 2024
2024
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2024
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C. S. Xia, M. Paltenghi, J. L. Tian, M. Pradel, and L. Zhang, “Fuzz4all: Universal fuzzing with large language models,” in ICSE , 2024
2024
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G. Ryan, S. Jain, M. Shang, S. Wang, X. Ma, M. K. Ramanathan, and B. Ray, “Code-aware prompting: A study of coverage guided test generation in regression setting using llm,” in FSE , 2024
2024
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2024
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S. Feng and C. Chen, “Prompting is all your need: Automated android bug replay with large language models,” in ICSE , 2024
2024
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2024
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Y. Zhang, H. Ruan, Z. Fan, and A. Roychoudhury, “Autocoderover: Autonomous program improvement,” 2024
2024
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J. Yang, C. E. Jimenez, K. Lieret, S. Yao, A. Wettig, K. Narasimhan, and O. Press, “Swe-agent: Agent-computer interfaces enable automated software engineering,” 2024
2024
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