FixMiner: Mining relevant fix patterns for automated program repair

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...

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Veröffentlicht in:	Empirical software engineering : an international journal Jg. 25; H. 3; S. 1980 - 2024
Hauptverfasser:	Koyuncu, Anil, Liu, Kui, Bissyandé, Tegawendé F., Kim, Dongsun, Klein, Jacques, Monperrus, Martin, Le Traon, Yves
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.05.2020 Springer Nature B.V
Schlagworte:	Automation Clustering Compilers Computer Science Context Debugging Empirical software engineering Fix patterns Interpreters Patches Patching Program repair Programming Languages Repair Scripts Software development Software Engineering/Programming and Operating Systems Source code Trees Fix patterns Program repair Empirical software engineering Debugging Patches
ISSN:	1382-3256, 1573-7616, 1573-7616
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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, PAR FixMiner , 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 PAR FixMiner ’s generated plausible patches are correct.
AbstractList	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, PAR(FixMiner), 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 PAR(FixMiner)'s generated plausible patches are correct. 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. 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, PAR FixMiner , 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 PAR FixMiner ’s generated plausible patches are correct.
Author	Kim, Dongsun Klein, Jacques Le Traon, Yves Monperrus, Martin Liu, Kui Koyuncu, Anil Bissyandé, Tegawendé F.
Author_xml	– sequence: 1 givenname: Anil surname: Koyuncu fullname: Koyuncu, Anil email: anil.koyuncu@uni.lu organization: SnT, University of Luxembourg – sequence: 2 givenname: Kui surname: Liu fullname: Liu, Kui organization: SnT, University of Luxembourg – sequence: 3 givenname: Tegawendé F. orcidid: 0000-0001-7270-9869 surname: Bissyandé fullname: Bissyandé, Tegawendé F. organization: SnT, University of Luxembourg – sequence: 4 givenname: Dongsun surname: Kim fullname: Kim, Dongsun organization: Furiosa.ai – sequence: 5 givenname: Jacques surname: Klein fullname: Klein, Jacques organization: SnT, University of Luxembourg – sequence: 6 givenname: Martin surname: Monperrus fullname: Monperrus, Martin organization: KTH Royal Institute of Technology – sequence: 7 givenname: Yves surname: Le Traon fullname: Le Traon, Yves organization: SnT, University of Luxembourg
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Snippet	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...
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SubjectTerms	Automation Clustering Compilers Computer Science Context Debugging Empirical software engineering Fix patterns Interpreters Patches Patching Program repair Programming Languages Repair Scripts Software development Software Engineering/Programming and Operating Systems Source code Trees
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Title	FixMiner: Mining relevant fix patterns for automated program repair
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