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Automated logging statement generation supports developers in documenting critical software runtime behavior.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th annual meeting of the Association for Computational Linguistics (ACL) , 2002, pp. 311–318
2002
Earlier work this paper cites.
C.-Y. Lin, “Rouge: A package for automatic evaluation of summaries,” in Text summarization branches out , 2004, pp. 74–81
2004
Earlier work this paper cites.
A. Balakrishnan and C. Schulze, “Code obfuscation literature survey,” CS701 Construction of compilers , vol. 19, p. 31, 2005
2005
Earlier work this paper cites.
W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan, “Detecting large-scale system problems by mining console logs,” in Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles (SOSP) , 2009, pp. 117–132
2009
Earlier work this paper cites.
D. Yuan, S. Park, and Y. Zhou, “Characterizing logging practices in open-source software,” in 2012 34th International Conference on Software Engineering (ICSE) . IEEE, 2012, pp. 102–112
2012
Earlier work this paper cites.
D. Yuan, J. Zheng, S. Park, Y. Zhou, and S. Savage, “Improving software diagnosability via log enhancement,” ACM Transactions on Computer Systems (TOCS) , vol. 30, no. 1, pp. 1–28, 2012
2012
Earlier work this paper cites.
D. Yuan, S. Park, P. Huang, Y. Liu, M. M. Lee, X. Tang, Y. Zhou, and S. Savage, “Be conservative: Enhancing failure diagnosis with proactive logging,” in Presented as part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI) , 2012, pp. 293–306
2012
Earlier work this paper cites.
Q. Fu, J. Zhu, W. Hu, J.-G. Lou, R. Ding, Q. Lin, D. Zhang, and T. Xie, “Where do developers log? an empirical study on logging practices in industry,” in Companion Proceedings of the 36th International Conference on Software Engineering (ICSE) , 2014, pp. 24–33
2014
Earlier work this paper cites.
W. Shang, Z. M. Jiang, B. Adams, A. E. Hassan, M. W. Godfrey, M. Nasser, and P. Flora, “An exploratory study of the evolution of communicated information about the execution of large software systems,” Journal of Software: Evolution and Process (J. Softw.: Evol. Process) , vol. 26, no. 1, pp. 3–26, 2014
2014
Earlier work this paper cites.
J. Zhu, P. He, Q. Fu, H. Zhang, M. R. Lyu, and D. Zhang, “Learning to log: Helping developers make informed logging decisions,” in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE) , vol. 1. IEEE, 2015, pp. 415–425
2015
Earlier work this paper cites.
R. Ding, H. Zhou, J.-G. Lou, H. Zhang, Q. Lin, Q. Fu, D. Zhang, and T. Xie, “Log2: A cost-aware logging mechanism for performance diagnosis,” in 2015 USENIX Annual Technical Conference (USENIX ATC) , 2015, pp. 139–150
2015
Earlier work this paper cites.
A. Pecchia, M. Cinque, G. Carrozza, and D. Cotroneo, “Industry practices and event logging: Assessment of a critical software development process,” in 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering (ICSE) , vol. 2. IEEE, 2015, pp. 169–178
2015
Earlier work this paper cites.
S. Lal, N. Sardana, and A. Sureka, “Logoptplus: Learning to optimize logging in catch and if programming constructs,” in 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC) , vol. 1. IEEE, 2016, pp. 215–220
2016
Earlier work this paper cites.
H. Li, W. Shang, and A. E. Hassan, “Which log level should developers choose for a new logging statement?” Empirical Software Engineering (ESE) , vol. 22, pp. 1684–1716, 2017
2017
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. F. Donaldson, H. Evrard, A. Lascu, and P. Thomson, “Automated testing of graphics shader compilers,” Proceedings of the ACM on Programming Languages (PACMPL) , vol. 1, no. OOPSLA, pp. 1–29, 2017
2017
Earlier work this paper cites.
X. Zhao, K. Rodrigues, Y. Luo, M. Stumm, D. Yuan, and Y. Zhou, “Log20: Fully automated optimal placement of log printing statements under specified overhead threshold,” in Proceedings of the 26th Symposium on Operating Systems Principles (SOSP) , 2017, pp. 565–581
2017
Earlier work this paper cites.
B. Chen and Z. M. Jiang, “Characterizing and detecting anti-patterns in the logging code,” in 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) . IEEE, 2017, pp. 71–81
2017
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.
P. He, Z. Chen, S. He, and M. R. Lyu, “Characterizing the natural language descriptions in software logging statements,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE) , 2018, pp. 178–189
2018
Earlier work this paper cites.
B. Chen, J. Song, P. Xu, X. Hu, and Z. M. Jiang, “An automated approach to estimating code coverage measures via execution logs,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE) , 2018, pp. 305–316
2018
Earlier work this paper cites.
Y. Wan, Z. Zhao, M. Yang, G. Xu, H. Ying, J. Wu, and P. S. Yu, “Improving automatic source code summarization via deep reinforcement learning,” in Proceedings of the 33rd ACM/IEEE international conference on automated software engineering (ASE) , 2018, pp. 397–407
2018
Earlier work this paper cites.
G. Zhao and J. Huang, “Deepsim: deep learning code functional similarity,” in Proceedings of the 2018 ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/SIGSOFT FSE 2018, Lake Buena Vista, FL, USA, November 04-09, 2018 , G. T. Leavens, A. Garcia, and C. S. Pasareanu, Eds. ACM, 2018, pp. 141–151. [Online]. Available: https://doi.org/10.1145/3236024.3236068
2018
Earlier work this paper cites.
S. Kabinna, C.-P. Bezemer, W. Shang, M. D. Syer, and A. E. Hassan, “Examining the stability of logging statements,” Empirical Software Engineering (ESE) , vol. 23, pp. 290–333, 2018
2018
Earlier work this paper cites.
B. Chen, “Improving the software logging practices in devops,” in 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) . IEEE, 2019, pp. 194–197
2019
Earlier work this paper cites.
Z. Liu, X. Xia, D. Lo, Z. Xing, A. E. Hassan, and S. Li, “Which variables should i log?” IEEE Transactions on Software Engineering (TSE) , vol. 47, no. 9, pp. 2012–2031, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
E. Quiring, A. Maier, K. Rieck et al. , “Misleading authorship attribution of source code using adversarial learning.” in USENIX Security Symposium (USENIX Security) , 2019, pp. 479–496
2019
Earlier work this paper cites.
H. Cheers, Y. Lin, and S. P. Smith, “Spplagiarise: A tool for generating simulated semantics-preserving plagiarism of java source code,” in 2019 IEEE 10th International conference on software engineering and service science (ICSESS) . IEEE, 2019, pp. 617–622
2019
Earlier work this paper cites.
JavaParser, “Javaparser,” Mar 2019. [Online]. Available: https://javaparser.org
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
L. Floridi and M. Chiriatti, “Gpt-3: Its nature, scope, limits, and consequences,” Minds and Machines , vol. 30, pp. 681–694, 2020
2020
Earlier work this paper cites.
B. Chen and Z. M. Jiang, “Studying the use of java logging utilities in the wild,” in Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (ICSE) , 2020, pp. 397–408
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Z. Li, T.-H. Chen, and W. Shang, “Where shall we log? studying and suggesting logging locations in code blocks,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2020, pp. 361–372
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Dai, Z. Luan, S. Huang, C. Fung, H. Wang, H. Yang, and D. Qian, “Reval: Recommend which variables to log with pre-trained model and graph neural network,” IEEE Transactions on Network and Service Management (TNSM) , 2022
2022
Later among the works it cites.
Y. Huo, Y. Su, C. Lee, and M. R. Lyu, “Semparser: A semantic parser for log analytics,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 881–893
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
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S. He, P. He, Z. Chen, T. Yang, Y. Su, and M. R. Lyu, “A survey on automated log analysis for reliability engineering,” ACM computing surveys (CSUR) , vol. 54, no. 6, pp. 1–37, 2021
2021
Cited alongside, same era.
S. Gholamian, “Leveraging code clones and natural language processing for log statement prediction,” in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2021, pp. 1043–1047
2021
Cited alongside, same era.
Z. Li, H. Li, T.-H. Chen, and W. Shang, “Deeplv: Suggesting log levels using ordinal based neural networks,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . IEEE, 2021, pp. 1461–1472
2021
Cited alongside, same era.
GitHub, “Github copilot: Parrot or crow? a first look at rote learning in github copilot suggestions.” Mar 2023. [Online]. Available: https://github.blog/2021-06-30-github-copilot-research-recitation/
2021
Cited alongside, same era.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021
2021
Cited alongside, same era.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Herbert-Voss, W. H. Guss, A. Nichol, A. Paino, N. Tezak, J. Tang, I. Babuschkin, S. Balaji, S. Jain, W. Saunders, C. Hesse, A. N. Carr, J. Leike, J. Achiam, V. Misra, E. Morikawa, A. Radford, M. Knight, M. Brundage, M. Murati, K. Mayer, P. Welinder, B. McGrew, D. Amodei, S. McCandlish, I. Sutskever, and W. Zaremba, “Evaluating large language models trained on code,” 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Closest in time.
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
2023
Closest in time.
——, “Github copilot: Your ai pair programmer,” Mar 2023. [Online]. Available: https://github.com/features/copilot
2023
Closest in time.
Amazon, “Codewhisperer,” Mar 2023. [Online]. Available: https://aws.amazon.com/cn/codewhisperer/
2023
Closest in time.
M. R. I. Rabin, A. Hussain, M. A. Alipour, and V. J. Hellendoorn, “Memorization and generalization in neural code intelligence models,” Information and Software Technology (Inf. Softw. Technol.) , vol. 153, p. 107066, 2023
2023
Closest in time.
2023
Closest in time.
OpenAI, “Chatgpt,” Mar 2023. [Online]. Available: https://openai.com/blog/chatgpt/
2023
Closest in time.
2023
Closest in time.
CodeGeeX, “Codegeex,” Mar 2023. [Online]. Available: https://models.aminer.cn/codegeex/blog/
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Tabnine, “Tabnine,” Mar 2023. [Online]. Available: https://www.tabnine.com/
2023
Closest in time.
Z. Ding, Y. Tang, X. Cheng, H. Li, and W. Shang, “Logentext-plus: Improving neural machine translation-based logging texts generation with syntactic templates,” ACM Trans. Softw. Eng. Methodol. , sep 2023, just Accepted. [Online]. Available: https://doi.org/10.1145/3624740
2023
Closest in time.
Y. Tan, D. Min, Y. Li, W. Li, N. Hu, Y. Chen, and G. Qi, “Can chatgpt replace traditional kbqa models? an in-depth analysis of the question answering performance of the gpt llm family,” in International Semantic Web Conference . Springer, 2023, pp. 348–367
2023
Closest in time.
2023
Closest in time.
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
Closest in time.
H. Zhang, Y. Pei, J. Chen, and S. H. Tan, “Statfier: Automated testing of static analyzers via semantic-preserving program transformations,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2023, pp. 237–249
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
OpenAI., “Openai embeddings,” Aug 2023. [Online]. Available: https://platform.openai.com/docs/guides/embeddings
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
J. H. Dawes, D. Shin, and D. Bianculli, “Towards log slicing,” in International Conference on Fundamental Approaches to Software Engineering . Springer Nature Switzerland Cham, 2023, pp. 249–259
2023
Closest in time.
G. Yu, P. Chen, P. Li, T. Weng, H. Zheng, Y. Deng, and Z. Zheng, “Logreducer: Identify and reduce log hotspots in kernel on the fly,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1763–1775
2023
Closest in time.
F. Shi, X. Chen, K. Misra, N. Scales, D. Dohan, E. H. Chi, N. Schärli, and D. Zhou, “Large language models can be easily distracted by irrelevant context,” in International Conference on Machine Learning . PMLR, 2023, pp. 31 210–31 227
2023
Closest in time.
aiXcoder, “aixcoder,” Mar 2023. [Online]. Available: https://www.aixcoder.com
2023
Closest in time.
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” Transactions of the Association for Computational Linguistics , vol. 12, pp. 157–173, 2024
2024
Closest in time.