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Automatic program repair (APR) aims to reduce the cost of manually fixing software defects.
Exploratory data analysis
Tukey J.W · 1977
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C4.5: Programs for machine learning
John Ross Quinlan · 1993
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Selection of relevant features and examples in machine learning
Avrim L. Blum and Pat Langley · 1997
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’dummy’ variables
N. R. Draper and H. Smith · 1998
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Greedy function approximation: A gradient boosting machine
Jerome Friedman · 2000
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Smote: Synthetic minority over-sampling technique
Nitesh Chawla, Kevin Bowyer, Lawrence Hall, and W. Kegelmeyer · 2002
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Randoop: Feedback-directed random testing for java
Carlos Pacheco and Michael D. Ernst · 2007
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Feature selection based f-score and aco algorithm in support vector machine
S. Ding · 2009
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Using mutation to automatically suggest fixes for faulty programs
V. Debroy and W. E. Wong · 2010
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Robust logitboost and adaptive base class (abc) logitboost
Ping Li · 2010
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Evosuite: automatic test suite generation for object-oriented software
Gordon Fraser and Andrea Arcuri · 2011
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Fine-grained and accurate source code differencing
Jean-Rémy Falleri, Floréal Morandat, Xavier Blanc, Matias Martinez, and Martin Monperrus · 2014
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Defects4j: A database of existing faults to enable controlled testing studies for java programs
Rene Just, Darioush Jalali, and Michael D Ernst · 2014
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Learning to combine multiple ranking metrics for fault localization
J. Xuan and M. Monperrus · 2014
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An analysis of patch plausibility and correctness for generate-and-validate patch generation systems
Zichao Qi, Fan Long, Sara Achour, and Martin Rinard · 2015
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Do automatically generated unit tests find real faults? an empirical study of effectiveness and challenges
Sina Shamshiri, Rene Just, Jose Miguel Rojas, Gordon Fraser, Phil McMinn, and Andrea Arcuri · 2015
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Is the cure worse than the disease? overfitting in automated program repair
Edward K. Smith, Earl T. Barr, Claire Le Goues, and Yuriy Brun · 2015
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Online defect prediction for imbalanced data
Ming Tan, Lin Tan, Sashank Dara, and Caleb Mayeux · 2015
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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DynaMoth: Dynamic Code Synthesis for Automatic Program Repair
Thomas Durieux and Martin Monperrus · 2016
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Automatic patch generation by learning correct code
Fan Long and Martin Rinard · 2016
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Automatic Repair of Real Bugs in Java: A Large-Scale Experiment on the Defects4J Dataset
Matias Martinez, Thomas Durieux, Romain Sommerard, Jifeng Xuan, and Martin Monperrus · 2016
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Anti-patterns in search-based program repair
Shin Hwei Tan, Hiroaki Yoshida, Mukul R. Prasad, and Abhik Roychoudhury · 2016
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Automatically learning semantic features for defect prediction
Song Wang, Taiyue Liu, and Lin Tan · 2016
Cited alongside, same era.
Nopol: Automatic repair of conditional statement bugs in java programs
Jifeng Xuan, Matias Martinez, Favio Demarco, Maxime Clément, Sebastian Lamelas, Thomas Durieux, Daniel Le Berre, and Martin Monperrus · 2016
Cited alongside, same era.
Dynamic Patch Generation for Null Pointer Exceptions Using Metaprogramming
Thomas Durieux, Benoit Cornu, Lionel Seinturier, and Martin Monperrus · 2017
Cited alongside, same era.
S3: Syntax- and semantic-guided repair synthesis via programming by examples
Xuan-Bach D. Le, Duc-Hiep Chu, David Lo, Claire Le Goues, and Willem Visser · 2017
Cited alongside, same era.
Automatic software repair: a bibliography
Martin Monperrus · 2017
Cited alongside, same era.
Learning to blame: Localizing novice type errors with data-driven diagnosis
Sequencer: Sequence-to-sequence learning for end-to-end program repair
Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, and Martin Monperrus · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Empirical review of java program repair tools: A large-scale experiment on 2,141 bugs and 23,551 repair attempts
Thomas Durieux, Fernanda Madeiral, Matias Martinez, and Rui Abreu · 2019
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Crash-avoiding program repair
Xiang Gao, Sergey Mechtaev, and Abhik Roychoudhury · 2019
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Practical program repair via bytecode mutation
Ali Ghanbari, Samuel Benton, and Lingming Zhang · 2019
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Automated program repair
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury · 2019
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Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, and Ranjit Jhala · 2017
Cited alongside, same era.
Fluccs: Using code and change metrics to improve fault localization
Jeongju Sohn and Shin Yoo · 2017
Cited alongside, same era.
Leveraging syntax-related code for automated program repair
Q. Xin and S. P. Reiss · 2017
Cited alongside, same era.
Identifying test-suite-overfitted patches through test case generation
Qi Xin and Steven P. Reiss · 2017
Cited alongside, same era.
Precise condition synthesis for program repair
Yingfei Xiong, Jie Wang, Runfa Yan, Jiachen Zhang, Shi Han, Gang Huang, and Lu Zhang · 2017
Cited alongside, same era.
Better test cases for better automated program repair
Jinqiu Yang, Alexey Zhikhartsev, Yuefei Liu, and Lin Tan · 2017
Cited alongside, same era.
Automatic feature learning for predicting vulnerable software components
H. K. Dam, T. Tran, T. T. M. Pham, S. W. Ng, J. Grundy, and A. Ghose · 2018
Cited alongside, same era.
Closest in time.
Deepjit: An end-to-end deep learning framework for just-in-time defect prediction
Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, and Naoyasu Ubayashi · 2019
Closest in time.
Precise learn-to-rank fault localization using dynamic and static features of target programs
Yunho Kim, Seokhyeon Mun, Shin Yoo, and Moonzoo Kim · 2019
Closest in time.
On reliability of patch correctness assessment
Xuan-Bach D. Le, Lingfeng Bao, David Lo, Xin Xia, and Shanping Li · 2019
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Bears: An Extensible Java Bug Benchmark for Automatic Program Repair Studies
Fernanda Madeiral, Simon Urli, Marcelo Maia, and Martin Monperrus · 2019
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Coming: a tool for mining change pattern instances from git commits
Matias Martinez and Martin Monperrus · 2019
Closest in time.
Automatically identifying code features for software defect prediction: Using ast n-grams
Thomas Shippey, David Bowes, and Tracy Hall · 2019
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Learning the relation between code features and code transforms with structured prediction
Zhongxing Yu, Matias Martinez, Tegawend Bissyand, and Martin Monperrus · 2019
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ODS’s repository that contains the models and the data used in this study
GitHub Experiment · 2020
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Validation of automatically generated patches: An appetizer, 2020
Ali Ghanbari · 2020
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Causal testing: Understanding defects’ root causes
Brittany Johnson, Yuriy Brun, and Alexandra Meliou · 2020
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Coconut: Combining context-aware neural translation models using ensemble for program repair
Thibaud Lutellier, Hung Viet Pham, Lawrence Pang, Yitong Li, Moshi Wei, and Lin Tan · 2020
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Type error feedback via analytic program repair
Georgios Sakkas, Madeline Endres, Benjamin Cosman, Westley Weimer, and Ranjit Jhala · 2020
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Evaluating representation learning of code changes for predicting patch correctness in program repair
Haoye Tian, Kui Liu, Abdoul Kader Kaboreé, Anil Koyuncu, Li Li, Jacques Klein, and Tegawendé F. Bissyandé · 2020
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Automated patch correctness assessment: How far are we?
Shangwen Wang, Ming Wen, Bo Lin, Hongjun Wu, Yihao Qin, Deqing Zou, Xiaoguang Mao, and Hai Jin · 2020
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Cpc: Automatically classifying and propagating natural language comments via program analysis
Xiangzhe Xu, Weifeng Zhang, Lin Tan, and Xiangyu Zhang · 2020
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Exploring the Differences between Plausible and Correct Patches at Fine-Grained Level
Bo Yang and Jinqiu Yang · 2020
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A comprehensive study of automatic program repair on the quixbugs benchmark
He Ye, Matias Martinez, Thomas Durieux, and Martin Monperrus · 2021
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Automated patch assessment for program repair at scale
He Ye, Matias Martinez, and Martin Monperrus · 2021
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