Dynamic patch generation for null pointer exceptions using metaprogramming

Durieux, Thomas; Cornu, Benoit; Seinturier, Lionel; Monperrus, Martin

doi:10.1109/saner.2017.7884635

Cited by 69 publications

(56 citation statements)

References 18 publications

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“…The most popular approach to automatically repair bugs is to create a patch using the test suite of the program as the specification of its expected behavior. This type of approach is known as test-suite-based program repair [30], which has been applied in several repair tools in the last decade [2,6,10,13,14,17,18,23,24,27,28,37,42,43,[45][46][47]50].…”

Section: Introductionmentioning

confidence: 99%

Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

Durieux

Madeiral

Martínez

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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122

124

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In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 5 benchmarks of bugs. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks of bugs. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts in total, which we used to find the causes of non-patch generation. These causes are reported in this paper, which can help repair tool designers to improve their techniques and tools.

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Section: Introductionmentioning

confidence: 99%

Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

Durieux

Madeiral

Martínez

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

Self Cite

122

124

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“…In this paper, we propose a novel technique to provide self‐healing for the web. It is along the line of previous work on self‐healing software , also called failure‐oblivious computing , automated recovery and remediation . The majority of the self‐healing literature focuses on the C/Unix runtime.…”

Section: Introductionmentioning

confidence: 93%

Fully Automated HTML and Javascript Rewriting for Constructing a Self-Healing Web Proxy

Durieux¹,

Hamadi²,

Monperrus³

2018

2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE)

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Summary Over the last few years, the complexity of web applications has increased to provide more dynamic web applications to users. The drawback of this complexity is the growing number of errors in the front‐end applications. In this paper, we present an approach to provide self‐healing for the web. We implemented this approach in two different tools: (i) BikiniProxy, an HTTP repair proxy, and (ii) BugBlock, a browser extension. They use five self‐healing strategies to rewrite the buggy HTML and JavaScript code to handle errors in web pages. We evaluate BikiniProxy and BugBlock with a new benchmark of 555 reproducible JavaScript errors of which 31.76% can be automatically self‐healed by BikiniProxy and 15.67% by BugBlock.

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“…A common, and reliable, strategy in automatic program repair is to generate concrete patches based on fix patterns [26] (also referred to as fix templates [51] or program transformation schemas [18]). Several APR systems [14,18,26,31,46,48,49,51,60,73] in the literature implement this strategy by using diverse sets of fix patterns obtained either via manual generation or automatic mining of bug fix datasets. In this work, we consider the pioneer PAR system by Kim et al [26].…”

Section: Fix Pattern-based Patch Generationmentioning

confidence: 99%

iFixR: bug report driven program repair

Koyuncu

Liu

Bissyandé

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is then the systematization of patch generation for user-reported bugs. Although this ambition is aligned with the momentum of automated program repair, the literature has, so far, mostly focused on generate-andvalidate setups where fault localization and patch generation are driven by a well-defined test suite. On the one hand, however, the common (yet strong) assumption on the existence of relevant test cases does not hold in practice for most development settings: many bugs are reported without the available test suite being able to reveal them. On the other hand, for many projects, the number of bug reports generally outstrips the resources available to triage them. Towards increasing the adoption of patch generation tools by practitioners, we investigate a new repair pipeline, iFixR, driven by bug reports: (1) bug reports are fed to an IR-based fault localizer;(2) patches are generated from fix patterns and validated via regression testing; (3) a prioritized list of generated patches is proposed to developers. We evaluate iFixR on the Defects4J dataset, which we enriched (i.e., faults are linked to bug reports) and carefullyreorganized (i.e., the timeline of test-cases is naturally split). iFixR generates genuine/plausible patches for 21/44 Defects4J faults with its IR-based fault localizer. iFixR accurately places a genuine/plausible patch among its top-5 recommendation for 8/13 of these faults (without using future test cases in generation-and-validation). CCS CONCEPTS• Software and its engineering → Software verification and validation; Software testing and debugging.

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Dynamic patch generation for null pointer exceptions using metaprogramming

Cited by 69 publications

References 18 publications

Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

Fully Automated HTML and Javascript Rewriting for Constructing a Self-Healing Web Proxy

iFixR: bug report driven program repair

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