Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, and Charles A. Sutton. 2018. A survey of machine learning for big code and naturalness. ACM Computing Surveys 51, 4 (2018), 81:1-81:37. DOI:https://doi.org/10.1145/3212695
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. code2vec: Learning distributed representations of code. Proceedings of the ACM on Programming Languages 3, POPL (2019), 40:1-40:29. DOI:https://doi.org/10.1145/3290353
Johannes Bader, Andrew Scott, Michael Pradel, and Satish Chandra. 2019. Getafix: Learning to fix bugs automatically. Proceedings of the ACM on Programming Languages 3, OOPSLA (2019), 159:1-159:27. DOI:https://doi.org/10. 1145/3360585
Earl T. Barr, Yuriy Brun, Premkumar T. Devanbu, Mark Harman, and Federica Sarro. 2014. The plastic surgery hypothesis. In Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, 306-317. DOI:https://doi.org/10.1145/2635868.2635898
Junjie Chen, Alastair F. Donaldson, Andreas Zeller, and Hongyu Zhang. 2017. Testing and verification of compilers (dagstuhl seminar 17502). Dagstuhl Reports 7, 12 (2017), 50-65. DOI:https://doi.org/10.4230/DagRep.7.12.50
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado,Wei Chai, Mustafa Ispir, et al. 2016. Wide and deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7-10.
Rhys Compton, Eibe Frank, Panos Patros, and Abigail Koay. 2020. Embedding Java classes with code2vec: Improvements from variable obfuscation. In Proceedings of the 17th Mining Software Repositories. ACM.
Viktor Csuvik, Dániel Horváth, Ferenc Horváth, and László Vidács. 2020. Utilizing source code embeddings to identify correct patches. In Proceedings of the 2nd International Workshop on Intelligent Bug Fixing. IEEE, 18-25. DOI:https:// doi.org/10.1109/IBF50092.2020.9034714
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171-4186. DOI:https://doi.org/10.18653/v1/ n19-1423
Thomas G. Dietterich. 1998. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 10, 7 (1998), 1895-1923.
Thomas Durieux, Fernanda Madeiral, Matias Martinez, and Rui Abreu. 2019. Empirical review of Java program repair tools: A large-scale experiment on 2,141 bugs and 23,551 repair attempts. In Proceedings of the 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 302-313. DOI:https://doi.org/10.1145/3338906.3338911
Chunrong Fang, Zixi Liu, Yangyang Shi, Jeff Huang, and Qingkai Shi. 2020. Functional code clone detection with syntax and semantics fusion learning. In Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 516-527. DOI:https://doi.org/10.1145/3395363.3397362
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, et al. 2020. CodeBERT: A pre-trained model for programming and natural languages. arXiv:2002.08155. Retrieved from https://arxiv.org/abs/2002.08155.
Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar T. Devanbu. 2012. On the naturalness of software. In Proceedings of the 34th International Conference on Software Engineering. IEEE, 837-847. DOI:https://doi. org/10.1109/ICSE.2012.6227135
Thong Hoang, Hong Jin Kang, Julia Lawall, and David Lo. 2020. CC2Vec: Distributed representations of code changes. In Proceedings of the 42nd International Conference on Software Engineering. ACM, 518-529. DOI:https://doi.org/10. 1145/3377811.3380361
Thong Hoang, Julia Lawall, Yuan Tian, Richard Jayadi Oentaryo, and David Lo. 2019. PatchNet: Hierarchical deep learning-based stable patch identification for the linux kernel. arXiv:1911.03576. Retrieved from https://arxiv.org/abs/ 1911.03576.
Jiajun Jiang, Luyao Ren, Yingfei Xiong, and Lingming Zhang. 2019. Inferring program transformations from singular examples via big code. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering. IEEE, 255-266. DOI:https://doi.org/10.1109/ASE.2019.00033
Jiajun Jiang, Yingfei Xiong, Hongyu Zhang, Qing Gao, and Xiangqun Chen. 2018. Shaping program repair space with existing patches and similar code. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 298-309. DOI:https://doi.org/10.1145/3213846.3213871
René Just, Darioush Jalali, and Michael D. Ernst. 2014. Defects4J: A database of existing faults to enable controlled testing studies for Java programs. In Proceedings of the 23rd International Symposium on Software Testing and Analysis. ACM, 437-440. DOI:https://doi.org/10.1145/2610384.2628055
Rafael-Michael Karampatsis and Charles A. Sutton. 2020. How often do single-statement bugs occur? The ManySStuBs4J dataset. In Proceedings of the 17th Mining Software Repositories.
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé, Dongsun Kim, Jacques Klein, Martin Monperrus, and Yves Le Traon. 2020. FixMiner: Mining relevant fix patterns for automated program repair. Empirical Software Engineering 25, 3 (2020), 1980-2024. DOI:https://doi.org/10.1007/s10664-019-09780-z
Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé, Dongsun Kim, Martin Monperrus, Jacques Klein, and Yves Le Traon. 2019. iFixR: Bug report driven program repair. In Proceedings of the 27the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM, 314-325. DOI:https://doi.org/10.1145/ 3338906.3338935
Quoc V. Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning. JMLR.org, 1188-1196.
Xuan-Bach D. Le, Lingfeng Bao, David Lo, Xin Xia, Shanping Li, and Corina Pasareanu. 2019. On reliability of patch correctness assessment. In Proceedings of the 41st International Conference on Software Engineering. IEEE, 524-535. DOI:https://doi.org/10.1109/ICSE.2019.00064
Xuan Bach D. Le, Ferdian Thung, David Lo, and Claire Le Goues. 2018. Overfitting in semantics-based automated program repair. Empirical Software Engineering 23, 5 (2018), 3007-3033. DOI:https://doi.org/10.1007/s10664-017-9577-2
Claire Le Goues, Neal Holtschulte, Edward K. Smith, Yuriy Brun, Premkumar Devanbu, Stephanie Forrest, andWestley Weimer. 2015. The ManyBugs and IntroClass benchmarks for automated repair of C programs. IEEE Transactions on Software Engineering 41, 12 (2015), 1236-1256. DOI:https://doi.org/10.1109/TSE.2015.2454513
Claire Le Goues, ThanhVu Nguyen, Stephanie Forrest, and Westley Weimer. 2012. GenProg: A generic method for automatic software repair. IEEE Transactions on Software Engineering 38, 1 (2012), 54-72. DOI:https://doi.org/10.1109/ TSE.2011.104
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019. Automated program repair. Communications of the ACM 62, 12 (2019), 56-65. DOI:https://doi.org/10.1145/3318162
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017. QuixBugs: A multi-lingual program repair benchmark set based on the Quixey Challenge. In Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity. ACM, 55-56. DOI:https:// doi.org/10.1145/3135932.3135941
Kui Liu, Dongsun Kim, Tegawendé F. Bissyandé, Tae-young Kim, Kisub Kim, Anil Koyuncu, Suntae Kim, and Yves Le Traon. 2019. Learning to spot and refactor inconsistent method names. In Proceedings of the 41st International Conference on Software Engineering. IEEE, 1-12. DOI:https://doi.org/10.1109/ICSE.2019.00019
Kui Liu, Dongsun Kim, Tegawendé F. Bissyandé, Shin Yoo, and Yves Le Traon. 2018. Mining fix patterns for findbugs violations. IEEE Transactions on Software Engineering (2018). DOI:https://doi.org/10.1109/TSE.2018.2884955
Kui Liu, Dongsun Kim, Anil Koyuncu, Li Li, Tegawendé F. Bissyandé, and Yves Le Traon. 2018. A closer look at real-world patches. In Proceedings of the 34th International Conference on Software Maintenance and Evolution. IEEE, 275-286. DOI:https://doi.org/10.1109/ICSME.2018.00037
Kui Liu, Anil Koyuncu, Tegawendé F. Bissyandé, Dongsun Kim, Jacques Klein, and Yves Le Traon. 2019. You cannot fix what you cannot find! an investigation of fault localization bias in benchmarking automated program repair systems. In Proceedings of the 12th IEEE International Conference on Software Testing, Verification, and Validation. IEEE, 102-113. DOI:https://doi.org/10.1109/ICST.2019.00020
Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawendé F. Bissyandé. 2019. AVATAR: fixing semantic bugs with fix patterns of static analysis violations. In Proceedings of the 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering. IEEE, 456-467. DOI:https://doi.org/10.1109/SANER.2019.8667970
Kui Liu, Anil Koyuncu, Dongsun Kim, and Tegawendé F. Bissyandé. 2019. TBar: Revisiting template-based automated program repair. In Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 31-42. DOI:https://doi.org/10.1145/3293882.3330577
Kui Liu, Anil Koyuncu, Kisub Kim, Dongsun Kim, and Tegawendé F. Bissyandé. 2018. LSRepair: Live search of fix ingredients for automated program repair. In Proceedings of the 25th Asia-Pacific Software Engineering Conference ERA Track. IEEE, 658-662. DOI:https://doi.org/10.1109/APSEC.2018.00085
Kui Liu, Li Li, Anil Koyuncu, Dongsun Kim, Zhe Liu, Jacques Klein, and Tegawendé F. Bissyandé. 2021. A critical review on the evaluation of automated program repair systems. Journal of Systems and Software 171 (2021), 110817. DOI:https://doi.org/10.1016/j.jss.2020.110817
Kui Liu, Shangwen Wang, Anil Koyuncu, Kisub Kim, Tegawendé F. Bissyandé, Dongsun Kim, Peng Wu, Jacques Klein, Xiaoguang Mao, and Yves Le Traon. 2020. On the efficiency of test suite based program repair: A systematic assessment of 16 automated repair systems for Java programs. In Proceedings of the 42nd International Conference on Software Engineering. ACM, 625-627. DOI:https://doi.org/10.1145/3377811.3380338
Fan Long and Martin Rinard. 2016. Automatic patch generation by learning correct code. In Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. ACM, 298-312. DOI:https://doi. org/10.1145/2837614.2837617
Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30. I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Curran Associates, Inc., 4765-4774. Retrieved from http://papers.nips.cc/paper/7062-a-unifiedapproach-to-interpreting-model-predictions.pdf.
Fernanda Madeiral, Thomas Durieux, Victor Sobreira, and Marcelo Maia. 2018. Towards an automated approach for bug fix pattern detection.
Fernanda Madeiral, Simon Urli, Marcelo Maia, and Martin Monperrus. 2019. BEARS: An extensible Java bug benchmark for automatic program repair studies. In Proceedings of the 26th International Conference on Software Analysis, Evolution, and Reengineering. IEEE, 468-478. DOI:https://doi.org/10.1109/SANER.2019.8667991
Henry B. Mann and Donald R. Whitney. 1947. On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics 18, 1 (1947), 50-60. DOI:https://doi.org/10.1214/aoms/1177730491
Matias Martinez and Martin Monperrus. 2015. Mining software repair models for reasoning on the search space of automated program fixing. Empirical Software Engineering 20, 1 (2015), 176-205. DOI:https://doi.org/10.1007/s10664-013-9282-8
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation ofword representations in vector space. arXiv:1301.3781. Retrieved from https://arxiv.org/abs/1301.3781.
Martin Monperrus. 2018. Automatic software repair: A bibliography. ACM Computing Surveys 51, 1 (2018), 17:1-17:24. DOI:https://doi.org/10.1145/3105906
Martin Monperrus. 2018. The living review on automated program repair. In Proceedings of the HAL/archives-ouvertes. fr, Technical Report.
Samuel Ndichu, Sangwook Kim, Seiichi Ozawa, Takeshi Misu, and Kazuo Makishima. 2019. A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors. Applied Soft Computing 84 (2019). DOI:https://doi.org/10.1016/j.asoc.2019.105721
Weiguo Pian, Hanyu Peng, Xunzhu Tang, Tiezhu Sun, Haoye Tian, Andrew Habib, Jacques Klein, and Tegawendé F. Bissyandé. 2022. MetaTPTrans: Ameta learning approach formultilingual code representation learning. arXiv:2206.06460. Retrieved from https://arxiv.org/abs/2206.06460.
Yuhua Qi, Xiaoguang Mao, Yan Lei, Ziying Dai, and Chengsong Wang. 2014. The strength of random search on automated program repair. In Proceedings of the 36th International Conference on Software Engineering. ACM, 254-265. DOI:https://doi.org/10.1145/2568225.2568254
Zichao Qi, Fan Long, Sara Achour, and Martin Rinard. 2015. An analysis of patch plausibility and correctness for generate-and-validate patch generation systems. In Proceedings of the 24th International Symposium on Software Testing and Analysis. ACM, 24-36. DOI:https://doi.org/10.1145/2771783.2771791
Ripon Saha, Yingjun Lyu,Wing Lam, Hiroaki Yoshida, and Mukul Prasad. 2018. Bugs.jar: A large-scale, diverse dataset of real-world java bugs. In Proceedings of the 15th IEEE/ACM International Conference on Mining Software Repositories. ACM, 10-13. DOI:https://doi.org/10.1145/3196398.3196473
Seemanta Saha, Ripon K. Saha, and Mukul R. Prasad. 2019. Harnessing evolution for multi-hunk program repair. In Proceedings of the 41st International Conference on Software Engineering. IEEE, 13-24. DOI:https://doi.org/10.1109/ICSE. 2019.00020
Edward K. Smith, Earl T. Barr, Claire Le Goues, and Yuriy Brun. 2015. Is the cure worse than the disease? Overfitting in automated program repair. In Proceedings of the 10th Joint Meeting on Foundations of Software Engineering. ACM, 532-543. DOI:https://doi.org/10.1145/2786805.2786825
Mauricio Soto and Claire Le Goues. 2018. Using a probabilistic model to predict bug fixes. In Proceedings of the 25th International Conference on Software Analysis, Evolution and Reengineering. IEEE, 221-231. DOI:https://doi.org/10.1109/ SANER.2018.8330211
Haoye Tian, Yinghua Li,Weiguo Pian, Abdoul Kader Kabore, Kui Liu, Andrew Habib, Jacques Klein, and Tegawendé F Bissyandé. 2022. Predicting patch correctness based on the similarity of failing test cases. ACM Transactions on Software Engineering and Methodology (2022).
Haoye Tian, Kui Liu, Abdoul Kader Kaboré, Anil Koyuncu, Li Li, Jacques Klein, and Tegawendé F Bissyandé. 2020. 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. IEEE, 981-992.
Haoye Tian, Xunzhu Tang, Andrew Habib, Shangwen Wang, Kui Liu, Xin Xia, Jacques Klein, and Tegawendé F. Bissyandé. 2022. 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/ACMInternational Conference on Automated Software Engineering. IEEE.
Shangwen Wang, Ming Wen, Bo Lin, Hongjun Wu, Yihao Qin, Deqing Zou, Xiaoguang Mao, and Hai Jin. 2020. Automated patch correctness assessment: How far are we?. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering. ACM.
Huihui Wei and Ming Li. 2017. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 3034-3040. DOI:https://doi.org/10.24963/ijcai.2017/423
Westley Weimer, Zachary P. Fry, and Stephanie Forrest. 2013. Leveraging program equivalence for adaptive program repair: Models and first results. In Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering. IEEE, 356-366. DOI:https://doi.org/10.1109/ASE.2013.6693094
Westley Weimer, ThanhVu Nguyen, Claire Le Goues, and Stephanie Forrest. 2009. Automatically finding patches using genetic programming. In Proceedings of the 31st International Conference on Software Engineering. IEEE, 364-374. DOI:https://doi.org/10.1109/ICSE.2009.5070536
Ming Wen, Junjie Chen, Rongxin Wu, Dan Hao, and Shing-Chi Cheung. 2018. Context-aware patch generation for better automated program repair. In Proceedings of the 40th International Conference on Software Engineering. ACM, 1-11. DOI:https://doi.org/10.1145/3180155.3180233
F. Wilcoxon. 1945. Individual comparisons by ranking methods. Biometrics Bulletin 1, 6 (1945), 80-83.
Qi Xin and Steven P. Reiss. 2017. Identifying test-suite-overfitted patches through test case generation. In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. ACM, 226-236. DOI:https://doi. org/10.1145/3092703.3092718
Yingfei Xiong, Xinyuan Liu, Muhan Zeng, Lu Zhang, and Gang Huang. 2018. Identifying patch correctness in testbased program repair. In Proceedings of the 40th International Conference on Software Engineering. ACM, 789-799. DOI:https://doi.org/10.1145/3183519.3183540
Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, and Lu Zhang. 2017. Precise condition synthesis for program repair. In Proceedings of the 39th IEEE/ACM International Conference on Software Engineering. IEEE, 416-426. DOI:https://doi.org/10.1109/ICSE.2017.45
Dapeng Yan, Kui Liu, Yuqing Niu, Li Li, Zhe Liu, Zhiming Liu, Jacques Klein, and Tegawendé F. Bissyandé. 2022. Crex: Predicting patch correctness in automated repair of C programs through transfer learning of execution semantics. Information and Software Technology 152 (2022), 107043. DOI:https://doi.org/10.1016/j.infsof.2022.107043
Bo Yang and Jinqiu Yang. 2020. Exploring the differences between plausible and correct patches at fine-grained level. In Proceedings of the 2nd International Workshop on Intelligent Bug Fixing. IEEE, 1-8. DOI:https://doi.org/10.1109/ IBF50092.2020.9034821
Jinqiu Yang, Alexey Zhikhartsev, Yuefei Liu, and Lin Tan. 2017. Better test cases for better automated program repair. In Proceedings of the 11th Joint Meeting on Foundations of Software Engineering. ACM, 831-841. DOI:https://doi.org/ 10.1145/3106237.3106274
He Ye, Jian Gu, Matias Martinez, Thomas Durieux, and Martin Monperrus. 2021. Automated classification of overfitting patches with statically extracted code features. IEEE Transactions on Software Engineering (2021).
He Ye, Matias Martinez, Thomas Durieux, and Martin Monperrus. 2019. A comprehensive study of automatic program repair on the QuixBugs benchmark. In Proceedings of the 1st International Workshop on Intelligent Bug Fixing. IEEE, 1-10. DOI:https://doi.org/10.1109/IBF.2019.8665475
He Ye, Matias Martinez, and Martin Monperrus. 2019. Automated patch assessment for program repair at scale. arXiv:1909.13694. Retrieved from https://arxiv.org/abs/1909.13694.
Zeping Yu, Rui Cao, Qiyi Tang, Sen Nie, Junzhou Huang, and Shi Wu. 2020. Order matters: Semantic-aware neural networks for binary code similarity detection. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, 1145-1152. DOI:https://doi.org/10.1609/aaai.v34i01.5466
Zhongxing Yu, Matias Martinez, Benjamin Danglot, Thomas Durieux, and Martin Monperrus. 2019. Alleviating patch overfitting with automatic test generation: A study of feasibility and effectiveness for the Nopol repair system. Empirical Software Engineering 24, 1 (2019), 33-67. DOI:https://doi.org/10.1007/s10664-018-9619-4
Gang Zhao and Jeff Huang. 2018. 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. 141-151.
Shufan Zhou, Beijun Shen, and Hao Zhong. 2019. Lancer: Your code tell me what you need. In Proceedings of the 34th IEEE/ACMInternational Conference on Automated Software Engineering. IEEE, 1202-1205. DOI:https://doi.org/10.1109/ ASE.2019.00137
