Michele Tufano
Cody Watson
Denys Poshyvanyk
Skip Abstract Section
Abstract
Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.
- Abdulkareem Alali, Huzefa H. Kagdi, and Jonathan I. Maletic. 2008. What’s a typical commit? A characterization of open source software repositories. In Proceedings of the 16th IEEE International Conference on Program Comprehension, ICPC 2008, Amsterdam, The Netherlands, June 10--13, 2008. 182--191. Google Scholar
Digital Library
- Miltiadis Allamanis. 2018. The adverse effects of code duplication in machine learning models of code. CoRR abs/1812.06469. http://arxiv.org/abs/1812.06469Google Scholar
- Miltiadis Allamanis, Earl T. Barr, Christian Bird, and Charles Sutton. 2015. Suggesting accurate method and class names. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’15). ACM, New York, NY, 38--49. Google ScholarDigital Library
- Giuliano Antoniol, Kamel Ayari, Massimiliano Di Penta, Foutse Khomh, and Yann-Gaël Guéhéneuc. 2008. Is it a bug or an enhancement?: A text-based approach to classify change requests. In Proceedings of the 2008 Conference of the Centre for Advanced Studies on Collaborative Research, October 27--30, 2008, Richmond Hill, Ontario, Canada. 23. Google ScholarDigital Library
- Andrea Arcuri and Xin Yao. 2008. A novel co-evolutionary approach to automatic software bug fixing. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, June 1--6, 2008, Hong Kong, China. 162--168.Google ScholarCross Ref
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2014). arxiv:1409.0473.Google Scholar
- Earl T. Barr, Yuriy Brun, Premkumar 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 (FSE’14). ACM, New York, NY, 306--317. Google ScholarDigital Library
- S. Bhatia and R. Singh. 2016. Automated correction for syntax errors in programming assignments using recurrent neural networks. CoRR abs/1603.06129 (2016).Google Scholar
- Nicolas Boulanger-Lewandowski, Yoshua Bengio, and Pascal Vincent. 2013. Audio chord recognition with recurrent neural networks. In ISMIR. Citeseer, 335--340.Google Scholar
- Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V. Le. 2017. Massive exploration of neural machine translation architectures. CoRR abs/1703.03906 (2017). arxiv:1703.03906.Google Scholar
- David Bingham Brown, Michael Vaughn, Ben Liblit, and Thomas Reps. 2017. The care and feeding of wild-caught mutants. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’17). ACM, New York, NY, 511--522. Google ScholarDigital Library
- Antonio Carzaniga, Alessandra Gorla, Andrea Mattavelli, Nicolò Perino, and Mauro Pezzè. 2013. Automatic recovery from runtime failures. In Proceedings of the 2013 International Conference on Software Engineering (ICSE’13). IEEE Press, Piscataway, NJ, 782--791. Google ScholarDigital Library
- Zimin Chen and Martin Monperrus. 2018. CodRep. https://github.com/KTH/CodRep-competition.Google Scholar
- Zimin Chen and Martin Monperrus. 2018. The CodRep Machine Learning on Source Code Competition. Technical Report 1807.03200. arXiv. http://arxiv.org/pdf/1807.03200Google Scholar
- Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR abs/1406.1078 (2014). arxiv:1406.1078Google Scholar
- Jean-Rémy Falleri, Floréal Morandat, Xavier Blanc, Matias Martinez, and Martin Monperrus. 2014. Fine-grained and accurate source code differencing. In ACM/IEEE International Conference on Automated Software Engineering, ASE’14, Vasteras, Sweden, September 15--19, 2014. 313--324. Google ScholarDigital Library
- Michael Fischer, Martin Pinzger, and Harald C. Gall. 2003. Populating a release history database from version control and bug tracking systems. In 19th International Conference on Software Maintenance (ICSM’03), The Architecture of Existing Systems, 22--26 September, 2003, Amsterdam, The Netherlands. 23. Google ScholarDigital Library
- Mark Gabel and Zhendong Su. 2010. A study of the uniqueness of source code. In Proceedings of the 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE’10). ACM, New York, NY, 147--156. Google ScholarDigital Library
- GitHub. 2010. GitHub Compare API. Retrieved from https://developer.github.com/v3/repos/commits/#compare-two-commits.Google Scholar
- C. Le Goues, W. Weimer, and S. Forrest. {n.d.}. Representations and operators for improving evolutionary software repair. (GECCO’12). Google ScholarDigital Library
- Alex Graves. 2012. Sequence transduction with recurrent neural networks. CoRR abs/1211.3711. arxiv:1211.3711 http://arxiv.org/abs/1211.3711Google Scholar
- Ilya Grigorik. 2012. GitHub Archive. Retrieved from https://www.githubarchive.org.Google Scholar
- Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018. Deep code search. In Proceedings of the 40th International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, May 27-- June 3, 2018. Google ScholarDigital Library
- Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016. Deep API learning. In Proceedings of the 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Seattle, WA, November 13-18, 2016. 631--642. Google ScholarDigital Library
- Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2017. DeepAM: Migrate APIs with multi-modal sequence to sequence learning. CoRR abs/1704.07734 (2017). arxiv:1704.07734. Google ScholarDigital Library
- Rahul Gupta, Aditya Kanade, and Shirish K. Shevade. 2018. Deep reinforcement learning for programming language correction. CoRR abs/1801.10467 (2018). arxiv:1801.10467.Google Scholar
- Hideaki Hata, Osamu Mizuno, and Tohru Kikuno. 2012. Bug prediction based on fine-grained module histories. Proceedings of the International Conference on Software Engineering (06 2012), 200--210. Google ScholarDigital Library
- Kim Herzig, Sascha Just, and Andreas Zeller. 2013. It’s not a bug, it’s a feature: How misclassification impacts bug prediction. In Proceedings of the 35th International Conference on Software Engineering, (ICSE’13), San Francisco, CA, May 18-26, 2013. 392--401. Google ScholarDigital Library
- Abram Hindle, Earl T. Barr, Zhendong Su, Mark Gabel, and Premkumar Devanbu. 2012. On the naturalness of software. In Proceedings of the 34th International Conference on Software Engineering (ICSE’12). IEEE Press, Piscataway, NJ, 837--847. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (Nov. 1997), 1735--1780. Google ScholarDigital Library
- Guoliang Jin, Linhai Song, Wei Zhang, Shan Lu, and Ben Liblit. 2011. Automated atomicity-violation fixing. In Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’11). ACM, New York, NY, 389--400. Google ScholarDigital Library
- Magne Jorgensen and Martin Shepperd. 2007. A systematic review of software development cost estimation studies. IEEE Trans. Softw. Eng. 33, 1 (Jan. 2007), 33--53. Google ScholarDigital Library
- 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 2014 International Symposium on Software Testing and Analysis (ISSTA’14). ACM, New York, NY, 437--440. Google ScholarDigital Library
- Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent continuous translation models. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Seattle, Washington, 1700--1709.Google Scholar
- Y. Ke, K. Stolee, C. Le Goues, and Y. Brun. {n.d.}. Repairing programs with semantic code search. ASE’15. Google ScholarDigital Library
- Dongsun Kim, Jaechang Nam, Jaewoo Song, and Sunghun Kim. 2013. Automatic patch generation learned from human-written patches. In Proceedings of the 35th International Conference on Software Engineering, (ICSE’13), San Francisco, CA, May 18-26, 2013. 802--811. Google ScholarDigital Library
- P. Koehn. 2010. Statistical Machine Translation. Google ScholarDigital Library
- Carsten Kolassa, Dirk Riehle, and Michel A. Salim. 2013. A model of the commit size distribution of open source. In SOFSEM 2013: Theory and Practice of Computer Science, Peter van Emde Boas, Frans C. A. Groen, Giuseppe F. Italiano, Jerzy Nawrocki, and Harald Sack (Eds.). Springer, Berlin, 52--66.Google Scholar
- An Ngoc Lam, Anh Tuan Nguyen, Hoan Anh Nguyen, and Tien N. Nguyen. 2017. Bug localization with combination of deep learning and information retrieval. In Proceedings of the 25th International Conference on Program Comprehension, ICPC 2017, Buenos Aires, Argentina, May 22-23, 2017. 218--229. Google ScholarDigital Library
- X. Le, D. Chu, D. Lo, C. Le Goues, and W. Visser. {n.d.}. S3: Syntax- and Semantic-guided Repair Synthesis via Programming by Examples (FSE’17).Google Scholar
- Xuan-Bach D. Le, David Lo, and Claire Le Goues. 2016. History driven program repair. In IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016, Suita, Osaka, Japan, March 14-18, 2016—Volume 1. 213--224.Google ScholarCross Ref
- Claire Le Goues, Michael Dewey-Vogt, Stephanie Forrest, and Westley Weimer. 2012. A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In 34th International Conference on Software Engineering, ICSE 2012, June 2--9, 2012, Zurich, Switzerland. 3--13. Google ScholarDigital Library
- C. Le Goues, N. Holtschulte, E. Smith, Y. Brun, P. Devanbu, S. Forrest, and W. Weimer. 2015. The ManyBugs and IntroClass benchmarks for automated repair of C programs. TSE 41, 12 (2015), 1236--1256.Google ScholarDigital Library
- Claire Le Goues, ThanhVu Nguyen, Stephanie Forrest, and Westley Weimer. 2012. GenProg: A generic method for automatic software repair. IEEE Trans. Software Eng. 38, 1 (2012), 54--72. Google ScholarDigital Library
- Daoyuan Li, Li Li, Dongsun Kim, Tegawendé F. Bissyandé, David Lo, and Yves Le Traon. 2016. Watch out for this commit! A study of influential software changes. CoRR abs/1606.03266 (2016). arxiv:1606.03266 http://arxiv.org/abs/1606.03266Google Scholar
- Fan Long, Peter Amidon, and Martin Rinard. 2017. Automatic inference of code transforms for patch generation. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’17). ACM, New York, NY, 727--739. Google ScholarDigital Library
- 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 (POPL’16). ACM, New York, NY, 298--312. Google ScholarDigital Library
- Minh-Thang Luong, Hieu Pham, and Christopher D. Manning. 2015. Effective approaches to attention-based neural machine translation. CoRR abs/1508.04025 (2015). arxiv:1508.04025.Google Scholar
- Matias Martinez, Thomas Durieux, Romain Sommerard, Jifeng Xuan, and Martin Monperrus. 2017. Automatic repair of real bugs in Java: A large-scale experiment on the Defects4J dataset. Empirical Software Engineering 22, 4 (2017), 1936--1964. Google ScholarDigital Library
- Matias Martinez, Westley Weimer, and Martin Monperrus. 2014. Do the fix ingredients already exist? An empirical inquiry into the redundancy assumptions of program repair approaches. In Companion Proceedings of the 36th International Conference on Software Engineering (ICSE Companion’14). ACM, New York, NY, 492--495. Google ScholarDigital Library
- S. Mechtaev, Y. Jooyong, and A. Roychoudhury. {n.d.}. Angelix: Scalable multiline program patch synthesis via symbolic analysis (ICSE’16). Google ScholarDigital Library
- S. Mechtaev, Y. Jooyong, and A. Roychoudhury. {n.d.}. DirectFix: Looking for simple program repairs (ICSE’15). Google ScholarDigital Library
- Martin Monperrus. 2018. Automatic software repair: A bibliography. ACM Comput. Surv. 51, 1, Article 17 (Jan. 2018), 24 pages. Google ScholarDigital Library
- Martin Monperrus and Matias Martinez. 2012. CVS-Vintage: A Dataset of 14 CVS Repositories of Java Software. (Dec. 2012). https://hal.archives-ouvertes.fr/hal-00769121 working paper or preprint.Google Scholar
- K. Moran, C. Bernal-Cárdenas, M. Curcio, R. Bonett, and D. Poshyvanyk. 2018. Machine learning-based prototyping of graphical user interfaces for mobile apps. IEEE Trans. Software Eng. (2018).Google Scholar
- Anh Tuan Nguyen, Hoan Anh Nguyen, Tung Thanh Nguyen, and Tien N. Nguyen. 2014. Statistical learning approach for mining API usage mappings for code migration. In Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering (ASE’14). ACM, New York, NY, 457--468. Google ScholarDigital Library
- Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N. Nguyen. 2013. Lexical statistical machine translation for language migration. In Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’13). ACM, New York, NY, 651--654. Google ScholarDigital Library
- Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N. Nguyen. 2014. Migrating code with statistical machine translation. In Companion Proceedings of the 36th International Conference on Software Engineering (ICSE Companion’14). ACM, New York, NY, 544--547. Google ScholarDigital Library
- Hoan Anh Nguyen, Anh Tuan Nguyen, Tung Thanh Nguyen, Tien N. Nguyen, and Hridesh Rajan. 2013. A study of repetitiveness of code changes in software evolution. In Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering (ASE’13). IEEE Press, Piscataway, NJ, 180--190. Google ScholarDigital Library
- Hoang Duong Thien Nguyen, Dawei Qi, Abhik Roychoudhury, and Satish Chandra. 2013. SemFix: Program repair via semantic analysis. In Proceedings of the 2013 International Conference on Software Engineering (ICSE’13). IEEE Press, Piscataway, NJ, 772--781. http://dl.acm.org/citation.cfm?id=2486788.2486890. Google ScholarDigital Library
- Terence Parr. 2013. The Definitive ANTLR 4 Reference (2nd ed.). Pragmatic Bookshelf. Google ScholarDigital Library
- Terence Parr and Kathleen Fisher. 2011. LL(*): The foundation of the ANTLR parser generator. In Proceedings of the 32nd ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’11). ACM, New York, NY, 425--436. Google ScholarDigital Library
- Jeff H. Perkins, Sunghun Kim, Sam Larsen, Saman Amarasinghe, Jonathan Bachrach, Michael Carbin, Carlos Pacheco, Frank Sherwood, Stelios Sidiroglou, Greg Sullivan, Weng-Fai Wong, Yoav Zibin, Michael D. Ernst, and Martin Rinard. 2009. Automatically patching errors in deployed software. In Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles (SOSP’09). ACM, New York, NY, 87--102. Google ScholarDigital Library
- Derrin Pierret and Denys Poshyvanyk. 2009. An empirical exploration of regularities in open-source software lexicons. In the 17th IEEE International Conference on Program Comprehension, ICPC 2009, Vancouver, British Columbia, Canada, May 17--19, 2009. 228--232.Google ScholarCross Ref
- Y. Pu, K. Narasimhan, A. Solar-Lezama, and R. Barzilay. {n.d.}. Sk_P: A Neural Program Corrector for MOOCs (SPLASH Companion 2016). Google ScholarDigital Library
- Z. Qi, F. Long, S. Achour, and M. Rinard. {n.d.}. An Analysis of Patch Plausibility and Correctness for Generate-and-validate Patch Generation Systems (ISSTA’15). Google ScholarDigital Library
- Veselin Raychev, Martin Vechev, and Eran Yahav. 2014. Code completion with statistical language models. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI’14). ACM, New York, NY, 419--428. Google ScholarDigital Library
- Chanchal Kumar Roy and James R. Cordy. 2008. NICAD: Accurate detection of near-miss intentional clones using flexible pretty-printing and code normalization. In the 16th IEEE International Conference on Program Comprehension, ICPC 2008, Amsterdam, The Netherlands, June 10--13, 2008. 172--181. Google ScholarDigital Library
- Hitesh Sajnani, Vaibhav Saini, Jeffrey Svajlenko, Chanchal K. Roy, and Cristina V. Lopes. 2016. SourcererCC: Scaling code clone detection to big-code. In Proceedings of the 38th International Conference on Software Engineering (ICSE’16). ACM, New York, NY, 1157--1168. Google ScholarDigital Library
- Ingo Scholtes, Pavlin Mavrodiev, and Frank Schweitzer. 2016. From aristotle to ringelmann: A large-scale analysis of team productivity and coordination in Open Source Software projects. Empirical Software Eng. 21, 2 (01 Apr. 2016), 642--683. Google ScholarDigital Library
- Robert C. Seacord, Daniel Plakosh, and Grace A. Lewis. 2003. Modernizing Legacy Systems: Software Technologies, Engineering Process and Business Practices. Addison-Wesley Longman Publishing Co., Inc., Boston, MA. Google ScholarDigital Library
- Stelios Sidiroglou-Douskos, Eric Lahtinen, Fan Long, and Martin Rinard. 2015. Automatic error elimination by horizontal code transfer across multiple applications. SIGPLAN Not. 50, 6 (June 2015), 43--54. Google ScholarDigital Library
- 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 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’15). ACM, New York, NY, 532--543. Google ScholarDigital Library
- Victor Sobreira, Thomas Durieux, Fernanda Madeiral Delfim, Martin Monperrus, and Marcelo de Almeida Maia. 2018. Dissection of a bug dataset: Anatomy of 395 patches from Defects4J. In 25th International Conference on Software Analysis, Evolution and Reengineering, SANER 2018, Campobasso, Italy, March 20--23, 2018. 130--140.Google ScholarCross Ref
- Mauricio Soto and Claire Le Goues. 2018. Using a probabilistic model to predict bug fixes. In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 221--231.Google ScholarCross Ref
- Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. CoRR abs/1409.3215 (2014). arxiv:1409.3215.Google ScholarDigital Library
- Yuchi Tian and Baishakhi Ray. 2017. Automatically diagnosing and repairing error handling bugs in C. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’17). ACM, New York, NY, 752--762. Google ScholarDigital Library
- Michele Tufano, Gabriele Bavota, Denys Poshyvanyk, Massimiliano Di Penta, Rocco Oliveto, and Andrea De Lucia. {n.d.}. An empirical study on developer-related factors characterizing fix-inducing commits. J. Software Evol. Process 29, 1 ({n.d.}), e1797.Google Scholar
- M. Tufano, J. Pantiuchina, C. Watson, G. Bavota, and D. Poshyvanyk. 2019. On learning meaningful code changes via neural machine translation. In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE’19). ACM, 12. Google ScholarDigital Library
- Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. Deep learning similarities from different representations of source code. In Proceedings of the 15th International Conference on Mining Software Repositories (MSR’18). ACM, New York, NY, 542--553. Google ScholarDigital Library
- Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE’18). ACM, New York, NY, 832--837. Google ScholarDigital Library
- Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. Online Appendix. Retrieved from https://sites.google.com/view/learning-fixes.Google Scholar
- Danny van Bruggen. 2014. JavaParser. Retrieved from https://javaparser.org/about.html.Google Scholar
- Song Wang, Taiyue Liu, and Lin Tan. 2016. Automatically learning semantic features for defect prediction. In Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, TX, May 14-22, 2016. 297--308. Google ScholarDigital Library
- 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 (ASE’13). IEEE Press, Piscataway, NJ, 356--366. Google ScholarDigital Library
- 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, ICSE 2009, May 16--24, 2009, Vancouver, Canada. 364--374. Google ScholarDigital Library
- Aaron Weiss, Arjun Guha, and Yuriy Brun. 2017. Tortoise: Interactive system configuration repair. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE’17). IEEE Press, Piscataway, NJ, 625--636. http://dl.acm.org/citation.cfm?id=3155562.3155641. Google ScholarDigital Library
- Cathrin Weiss, Rahul Premraj, Thomas Zimmermann, and Andreas Zeller. 2007. How long will it take to fix this bug? In Proceedings of the 4th International Workshop on Mining Software Repositories (MSR’07). IEEE Computer Society, Washington, D.C., 1--. Google ScholarDigital Library
- Marty White, Michele Tufano, M. Martinez, M. Monperrus, and D. Poshyvanyk. 2019. Sorting and transforming program repair ingredients via deep learning code similarities. In 2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, to appear.Google Scholar
- Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk. 2016. Deep learning code fragments for code clone detection. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, ASE 2016, Singapore, September 3--7, 2016. 87--98. Google ScholarDigital Library
- M. White, C. Vendome, M. Linares-Vásquez, and D. Poshyvanyk. {n.d.}. Toward deep learning software repositories (MSR’15). Google ScholarDigital Library
- J. Xuan, M. Martínez, F. DeMarco, M. Clément, S. Lamelas, T. Durieux, Daniel Le Berre, and M. Monperrus. 2016. Nopol: Automatic repair of conditional statement bugs in Java programs. IEEE Trans. Software Eng. 43, 1 (2016), 34--55. Google ScholarDigital Library
- Jinqiu Yang, Alexey Zhikhartsev, Yuefei Liu, and Lin Tan. 2017. Better test cases for better automated program repair. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE’17). ACM, New York, NY, 831--841. Google ScholarDigital Library
- Hao Zhong and Zhendong Su. 2015. An empirical study on real bug fixes. In Proceedings of the 37th International Conference on Software Engineering—Volume 1 (ICSE’15). IEEE Press, Piscataway, NJ, 913--923. http://dl.acm.org/citation.cfm?id=2818754.2818864. Google ScholarDigital Library
- Hao Zhong, Suresh Thummalapenta, Tao Xie, Lu Zhang, and Qing Wang. 2010. Mining API mapping for language migration. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering—Volume 1 (ICSE’10). ACM, New York, NY, 195--204. Google ScholarDigital Library
- Jian Zhou, Hongyu Zhang, and David Lo. 2012. Where should the bugs be fixed?—More accurate information retrieval-based bug localization based on bug reports. In Proceedings of the 34th International Conference on Software Engineering (ICSE’12). IEEE Press, Piscataway, NJ, 14--24. http://dl.acm.org/citation.cfm?id=2337223.2337226. Google ScholarDigital Library
Index Terms
- An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
Recommendations
An empirical investigation into learning bug-fixing patches in the wild via neural machine translation
ASE '18: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software EngineeringMillions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an ...
An Empirical Study of Bug Fixing Rate
COMPSAC '15: Proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 02Bug fixing is one of the most important activities in software development and maintenance. A software project often employs an issue tracking system such as Bugzilla to store and manage their bugs. In the issue tracking system, many bugs are invalid ...
An Empirical Study on Factors Impacting Bug Fixing Time
WCRE '12: Proceedings of the 2012 19th Working Conference on Reverse EngineeringFixing bugs is an important activity of the software development process. A typical process of bug fixing consists of the following steps: 1) a user files a bug report, 2) the bug is assigned to a developer, 3) the developer fixes the bug, 4) changed ...
Comments