Semantic program repair using a reference implementation

Mechtaev, Sergey; Nguyen, Manh-Dung; Noller, Yannic; Grunske, Lars; Roychoudhury, Abhik

doi:10.1145/3180155.3180247

Cited by 70 publications

(36 citation statements)

References 47 publications

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“…There has been recent research on such regression verification problems, in which the verification obligation is, not to prove the system fully correct, but merely to demonstrate the absence of regression with respect to a reference implementation [53]. Such regression verification approaches have also recently found application in automated bug fixing [99], and so we can be optimistic that they may find application, both for FiFiVerify and in FiGiVerify.…”

Section: A Figiverify: Find Genetically Improve and Verifymentioning

confidence: 99%

From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis

Harman

O’Hearn

2018

2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)

111

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This paper 1 describes some of the challenges and opportunities when deploying static and dynamic analysis at scale, drawing on the authors' experience with the Infer and Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, impacting billions of people worldwide.The paper identifies open problems that have yet to receive significant attention from the scientific community, yet which have potential for profound real world impact, formulating these as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects.

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Section: A Figiverify: Find Genetically Improve and Verifymentioning

confidence: 99%

From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis

Harman

O’Hearn

2018

2018 IEEE 18th International Working Conference on Source Code Analysis and Manipulation (SCAM)

111

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“…b) Program specific Test Generation Technique: In order to strengthen our patch correctness assessment, we consider a second random testing approach called InputSampling. In-putSampling, as an implementation of random testing [2] for QuixBugs, samples the input space according to a uniform distribution, and that uses the reference version as oracle [23]. If the reference version throws an exception on a generated input, the input is considered as invalid because not satisfying the preconditions, and is not kept.…”

Section: B Methodologymentioning

confidence: 99%

A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark

Yang

Martínez

Durieux

et al. 2019

2019 IEEE 1st International Workshop on Intelligent Bug Fixing (IBF)

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Automatic program repair papers tend to repeatedly use the same benchmarks. This poses a threat to the external validity of the findings of the program repair research community. In this paper, we perform an automatic repair experiment on a benchmark called QuixBugs that has never been studied in the context of program repair. In this study, we report on the characteristics of QuixBugs, and study five repair systems, Arja, Astor, Nopol, NPEfix and RSRepair, which are representatives of generate-and-validate repair techniques and synthesis repair techniques. We propose three patch correctness assessment techniques to comprehensively study overfitting and incorrect patches. Our key results are: 1) 15 / 40 buggy programs in the QuixBugs can be repaired with a test-suite adequate patch; 2) a total of 64 plausible patches for those 15 buggy programs in the QuixBugs are present in the search space of the considered tools; 3) the three patch assessment techniques discard in total 33 / 64 patches that are overfitting. This sets a baseline for future research of automatic repair on QuixBugs. Our experiment also highlights the major properties and challenges of how to perform automated correctness assessment of program repair patches. All experimental results are publicly available on Github in order to facilitate future research on automatic program repair.

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“…The current momentum of Automated Program Repair (APR) has led to the development of various approaches in the literature [1]- [16]. In the software engineering community, the focus is mainly placed on fixing semantic bugs, i.e., those bugs that make the program behavior deviate from developer's intention [1], [17]. Such bugs are detected by test suites.…”

Section: Introductionmentioning

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)

116

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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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Semantic program repair using a reference implementation

Cited by 70 publications

References 47 publications

From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis

From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis

A Comprehensive Study of Automatic Program Repair on the QuixBugs Benchmark

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

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