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Automated software debugging is a crucial task for improving the productivity of software developers.
Neural program repair by jointly learning to localize and repair
Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2019 · 1904
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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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 · 1959
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On the accuracy of spectrum-based fault localization
Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2007 · 2007
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Spectrum-based multiple fault localization
Rui Abreu, Peter Zoeteweij, and Arjan JC Van Gemund. 2009 · 2009
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Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al. 2020 · 2009
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An analysis and survey of the development of mutation testing
Yue Jia and Mark Harman. 2010 · 2010
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Theory and practice, do they match? a case with spectrum-based fault localization
Tien-Duy B Le, Ferdian Thung, and David Lo. 2013 · 2013
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A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization
Xiaoyuan Xie, Tsong Yueh Chen, Fei-Ching Kuo, and Baowen Xu. 2013 · 2013
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Metallaxis-fl: mutation-based fault localization
Mike Papadakis and Yves Le Traon. 2015 · 2015
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015 · 2015
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On the naturalness of software
Abram Hindle, Earl T Barr, Mark Gabel, Zhendong Su, and Premkumar Devanbu. 2016 · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
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On the" naturalness" of buggy code
Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, and Premkumar Devanbu. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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How many of all bugs do we find? a study of static bug detectors
Andrew Habib and Michael Pradel. 2018 · 2018
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PyDriller: Python framework for mining software repositories
Davide Spadini, Maurício Aniche, and Alberto Bacchelli. 2018 · 2018
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An empirical investigation into learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018 · 2018
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Predictive mutation testing
Jie Zhang, Lingming Zhang, Mark Harman, Dan Hao, Yue Jia, and Lu Zhang. 2018 · 2018
Self-supervised bug detection and repair
Miltiadis Allamanis, Henry Jackson-Flux, and Marc Brockschmidt. 2021 · 2021
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Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations
Nghi DQ Bui, Yijun Yu, and Lingxiao Jiang. 2021 · 2021
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Ahmed Elnaggar, Wei Ding, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Silvia Severini, Florian Matthes, and Burkhard Rost. 2021 · 2021
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Learning to complete code with sketches
Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, and Miltiadis Allamanis. 2021 · 2021
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Cure: Code-aware neural machine translation for automatic program repair
Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021 · 2021
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An empirical study of fault localization families and their combinations
Daming Zou, Jingjing Liang, Yingfei Xiong, Michael D Ernst, and Lu Zhang. 2019 · 2019
Cited alongside, same era.
Patching as translation: the data and the metaphor
Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu, and Vincent J Hellendoorn. 2020 · 2020
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CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
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Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
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How often do single-statement bugs occur? the manysstubs4j dataset
Rafael-Michael Karampatsis and Charles Sutton. 2020 · 2020
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Dlfix: Context-based code transformation learning for automated program repair
Yi Li, Shaohua Wang, and Tien N Nguyen. 2020 · 2020
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CodeXGLUE: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie LIU. 2021 · 2021
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Applying codebert for automated program repair of java simple bugs
Ehsan Mashhadi and Hadi Hemmati. 2021 · 2021
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How could neural networks understand programs?
Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, and Tie-Yan Liu. 2021 · 2021
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Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
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Break-it-fix-it: Unsupervised learning for program repair
Michihiro Yasunaga and Percy Liang. 2021 · 2021
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A syntax-guided edit decoder for neural program repair
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021 · 2021
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Linevul: A transformer-based line-level vulnerability prediction
Michael Fu and Chakkrit Tantithamthavorn. 2022 · 2022
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On distribution shift in learning-based bug detectors
Jingxuan He, Luca Beurer-Kellner, and Martin Vechev. 2022 · 2022
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Deeplinedp: Towards a deep learning approach for line-level defect prediction
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Tssb-3m: Mining single statement bugs at massive scale
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