Anti-patterns in search-based program repair

Tan, Shin Hwei; Yoshida, Hiroaki; Prasad, Munaga V. N. K.; Roychoudhury, Abhik

doi:10.1145/2950290.2950295

Cited by 110 publications

(44 citation statements)

References 20 publications

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“…Note that our aim is very different from that of ref. [38]. The rules considered in our study disallow definitely unnecessary operations rather than likely unpromising ones, so they generally do not restrict the expressive power of GP.…”

Section: Lack Of Adequate Prior Knowledgementioning

confidence: 95%

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ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

Yuan

Banzhaf

2020

IIEEE Trans. Software Eng.

138

139

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Automated program repair is the problem of automatically fixing bugs in programs in order to significantly reduce the debugging costs and improve the software quality. To address this problem, test-suite based repair techniques regard a given test suite as an oracle and modify the input buggy program to make the whole test suite pass. GenProg is well recognized as a prominent repair approach of this kind, which uses genetic programming (GP) to rearrange the statements already extant in the buggy program. However, recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.

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Section: Lack Of Adequate Prior Knowledgementioning

confidence: 95%

“…Oliveira et al [35] presented a low-granularity patch representation and developed several crossover operators associated with this representation. Tan et al [38] suggested a set of anti-patterns to inhibit GenProg or the other search-based methods from generating nonsensical patches.…”

Section: Search-based Repair Approachesmentioning

confidence: 99%

ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

Yuan

Banzhaf

2020

IIEEE Trans. Software Eng.

138

139

View full text Add to dashboard Cite

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“…Tan et al [39] integrated anti-patterns into two existing search-based automated program repair tools (namely, Gen-Prog [3] and SPR [8]) to help alleviate the problem of incorrect or incomplete fixes resulting from program repair. In their study, the anti-patterns are defined by themselves and limited to the control flow graph.…”

Section: Related Workmentioning

confidence: 99%

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Liu

Koyuncu

Kim

et al. 2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

117

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Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-theart pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.Index Terms-Automated program repair, static analysis, fix pattern.

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“…Zhong and Su conducted a large‐scale study on bug fixing changes in open source projects. Tan et al analyzed anti‐patterns that may interfere with the process of automated program repair. While most pattern discovery studies focused on statement‐level (ie, coarse‐grained) change patterns, Liu et al investigated fine‐grained (eg, expression‐level) code changes to discover better change elements for program repair.…”

Section: Related Workmentioning

confidence: 99%

Watch out for this commit! A study of influential software changes

Kim

et al. 2019

J Software Evolu Process

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One single code change can significantly influence a wide range of software systems and their users. For example, (a) adding a new feature can spread defects in several modules, while (b) changing an API method can improve the performance of all client programs. Unfortunately, developers often may not clearly know whether code changes are influential at commit time. This paper investigates influential software changes and proposes an approach to identify them immediately when they are applied. Our goals are to (a) identify existing influential changes (ICs) in software projects, (b) understand their characteristics, and (c) build a classification model of ICs to help developers find and address them early. We first conduct a post‐mortem analysis to discover existing influential changes by using intuitions (eg, changes referred by other changes). Then, we re‐categorize all identified changes through an open‐card sorting process. Subsequently, we conduct a survey with about 100 developers to finalize a taxonomy. Finally, from our ground truth, we extract features, including metrics such as the complexity of changes and file centrality in co‐change graphs to build machine learning classifiers. The experiment results show that our classification model with random samples achieves 86.8% precision, 74% recall, and 80.4% F‐measure, respectively.

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Anti-patterns in search-based program repair

Cited by 110 publications

References 20 publications

ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

ARJA: Automated Repair of Java Programs via Multi-Objective Genetic Programming

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Watch out for this commit! A study of influential software changes

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