Improving fault localization with pre-training

References

[1] Wong W E, Gao R, Li Y, Abreu R, Wotawa F . A survey on software fault localization. IEEE Transactions on Software Engineering, 2016, 42( 8): 707–740

[2] Feng Z, Gu D, Tang D, Duan N, Feng X, Gong M, Shou L, Qin B, Liu T, Jiang D, Zhou M. CodeBERT: a pre-trained model for programming and natural languages. In: Proceedings of Findings of the Association for Computational Linguistics. 2020, 1536–1547

[3] Sohn J, Yoo S. FLUCCS: using code and change metrics to improve fault localization. In: Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. 2017, 273–283

[4] Zhang Z, Lei Y, Mao X, Yan M, Xu L, Zhang X . A study of effectiveness of deep learning in locating real faults. Information and Software Technology, 2021, 131: 106486

[5] Pearson S, Campos J, Just R, Fraser G, Abreu R, Ernst M D, Pang D, Keller B. Evaluating and improving fault localization. In: Proceedings of the 39th IEEE/ACM International Conference on Software Engineering. 2017, 609–620

[6] Li X, Li W, Zhang Y, Zhang L. DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization. In: Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. 2019, 169–180

[7] Yan Y, Cheng D, Feng J E, Li H, Yue J . Survey on applications of algebraic state space theory of logical systems to finite state machines. Science China Information Sciences, 2023, 66( 1): 111201

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