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An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Authors:

Michele Tufano,

Gabriele Bavota,

Massimiliano Di Penta,

Denys PoshyvanykAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 28, Issue 4

Article No.: 19, Pages 1 - 29

https://doi.org/10.1145/3340544

Published: 02 September 2019 Publication History

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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.

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Index Terms

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software

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Published In

cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 28, Issue 4

October 2019

231 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3360049

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Association for Computing Machinery

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Publication History

Published: 02 September 2019

Accepted: 01 May 2019

Revised: 01 February 2019

Received: 01 September 2018

Published in TOSEM Volume 28, Issue 4

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Swiss National Science Foundation for the CCQR project

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Cao HHan DChu YTian FWang YLiu YJia JGe H(2024)Multi-mechanism neural machine translation framework for automatic program repairJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23403746:4(7859-7873)Online publication date: 18-Apr-2024
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Kim YPark YHan SYi JFilkov VRay BZhou M(2024)Enhancing the Efficiency of Automated Program Repair via Greybox AnalysisProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695602(1719-1731)Online publication date: 27-Oct-2024
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Yu XLi CPan MLi XFilkov VRay BZhou M(2024)DroidCoder: Enhanced Android Code Completion with Context-Enriched Retrieval-Augmented GenerationProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695063(681-693)Online publication date: 27-Oct-2024
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Lu JWang HLiu ZLiang KBao LYang XFilkov VRay BZhou M(2024)Instructive Code Retriever: Learn from Large Language Model's Feedback for Code Intelligence TasksProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3694997(191-203)Online publication date: 27-Oct-2024
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