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Automated Program Repair (APR) helps improve the efficiency of software development and maintenance.
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IntroClassJava: A Benchmark of 297 Small and Buggy Java Programs
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Nopol: Automatic repair of conditional statement bugs in java programs
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Getafix: Learning to fix bugs automatically
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SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair
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Contract-based program repair without the contracts. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 637–647
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Automatic Software Repair: A Survey
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QuixBugs: a multi-lingual program repair benchmark set based on the quixey challenge. 55–56
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Automatic inference of code transforms for patch generation. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . 727–739
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Automatic Software Repair: a Bibliography
Martin Monperrus. 2017 · 2017
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Abstract Syntax Networks for Code Generation and Semantic Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers , Regina Barzilay and Min-Yen Kan (Eds.). Association for Computational Linguistics, 1139–1149
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Learning syntactic program transformations from examples. In Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, May 20-28, 2017 , Sebastián Uchitel, Alessandro Orso, and Martin P. Robillard (Eds.). IEEE / ACM, 404–415
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Automated program repair
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019 · 2019
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Inferring program transformations from singular examples via big code. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 255–266
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A Grammar-Based Structural CNN Decoder for Code Generation. 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, 7055–7062
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A Structural Model for Contextual Code Changes
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Hoppity: Learning Graph Transformation to Detect and Fix Bugs in Programs. In International Conference on Learning Representations
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CoCoNuT: combining context-aware neural translation models using ensemble for program repair. In ISSTA ’20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis
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TreeGen: A Tree-Based Transformer Architecture for Code Generation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020 . AAAI Press, 8984–8991
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NLocalSAT: Boosting Local Search with Solution Prediction
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Beyond Tests: Program Vulnerability Repair via Crash Constraint Extraction
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Extracting Concise Bug-Fixing Patches from Human-Written Patches in Version Control Systems. In 43rd IEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid, Spain, 22-30 May 2021 . IEEE, 686–698
Yanjie Jiang, Hui Liu, Nan Niu, Lu Zhang, and Yamin Hu. 2021 · 2021
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