Reference : FixMiner: Mining relevant fix patterns for automated program repair

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/44172

Title :	FixMiner: Mining relevant fix patterns for automated program repair
Language :	English
Author, co-author :	Koyuncu, Anil [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Liu, Kui [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bissyande, Tegawendé François D Assise [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Kim, Dongsun [Furiosa.ai]
	Klein, Jacques [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
	Monperrus, Martin [KTH Royal Institute of Technology]
	Le Traon, Yves [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	14-Mar-2020
Journal title :	Empirical Software Engineering
Publisher :	Kluwer Academic Publishers
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	1382-3256
e-ISSN :	1573-7616
Country :	Netherlands
Keywords :	[en] Fix patterns ; Patches ; Program repair ; Debugging ; Mining
Abstract :	[en] 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 ASTlevel 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.
Permalink :	http://hdl.handle.net/10993/44172
DOI :	10.1007/s10664-019-09780-z
FnR project :	FnR ; FNR10449467 > Tegawendé François D'Assise Bissyandé > RECOMMEND > Automatic Bug Fix Recommendation: Improving Software Repair and Reducing Time-to-Fix Delays in Software Development Projects > 01/02/2016 > 31/01/2019 > 2015

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