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Empirical Software Engineering
Erschienen in: Empirical Software Engineering 3/2020

14.03.2020

FixMiner: Mining relevant fix patterns for automated program repair

verfasst von: Anil Koyuncu, Kui Liu, Tegawendé F. Bissyandé, Dongsun Kim, Jacques Klein, Martin Monperrus, Yves Le Traon

Erschienen in: Empirical Software Engineering | Ausgabe 3/2020

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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.
Vorheriger Artikel Ammonia: an approach for deriving project-specific bug patterns
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Fußnoten
1
The initial version of this paper was written concurrently to SimFix and CapGen.
 
3
The order of AST subtrees follows the order of hunks of the GNU diff format.
 
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
 
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Metadaten
Titel
FixMiner: Mining relevant fix patterns for automated program repair
verfasst von
Anil Koyuncu
Kui Liu
Tegawendé F. Bissyandé
Dongsun Kim
Jacques Klein
Martin Monperrus
Yves Le Traon
Publikationsdatum
14.03.2020
Verlag
Springer US
Erschienen in
Empirical Software Engineering / Ausgabe 3/2020
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI
https://doi.org/10.1007/s10664-019-09780-z

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