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FixMiner: Mining relevant fix patterns for automated program repair

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Abstract

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner’s generated plausible patches are correct.

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Notes

  1. The initial version of this paper was written concurrently to SimFix and CapGen.

  2. https://github.com/gwtproject/gwt/issues/676

  3. The order of AST subtrees follows the order of hunks of the GNU diff format.

  4. https://commons.apache.org/proper/commons-text/

  5. In this experiment, we excluded 34 patches from Defects4J dataset which affect more than 1 file.

  6. Semantic Patch Language

  7. We used GZoltar version 0.1.1

  8. Version 1.2.0 - https://github.com/rjust/defects4j/releases/tag/v1.2.0

  9. https://github.com/xuanbachle/bugfixes/blob/master/fixed.txt

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Acknowledgements

This work is supported by the Fonds National de la Recherche (FNR), Luxembourg, through RECOMMEND 15/IS/10449467 and FIXPATTERN C15/IS/9964569.

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Authors and Affiliations

  1. SnT, University of Luxembourg, Luxembourg City, Luxembourg

    Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé, Jacques Klein & Yves Le Traon

  2. Furiosa.ai, 145 Dosan-daero, Gangnam-gu Seoul, South Korea

    Dongsun Kim

  3. KTH Royal Institute of Technology, Stockholm, Sweden

    Martin Monperrus

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  1. Anil Koyuncu
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Correspondence to Anil Koyuncu.

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Koyuncu, A., Liu, K., Bissyandé, T.F. et al. FixMiner: Mining relevant fix patterns for automated program repair. Empir Software Eng 25, 1980–2024 (2020). https://doi.org/10.1007/s10664-019-09780-z

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