From Quick Fixes to Slow Fixes: Reimagining Static Analysis Resolutions to Enable Design Space Exploration

Barik, Titus; Song, Yoonki; Murphy-Hill, Emerson

doi:10.1109/icsme.2016.63

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…Our work in the development of FixBugs (Figure 3) demonstrates that it is feasible to retrofit existing compilers to expose explanatory features to the developer. 3 To understand how developers might benefit when toolsmiths move beyond speed as a primary metric for applying fixes to anomalies, we conducted an experiment using FixBugs [7].…”

Section: Resultsmentioning

confidence: 99%

How should static analysis tools explain anomalies to developers?

Barik

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Despite the advanced static analysis tools available within modern integrated development environments (IDEs), the error messages these tools produce remain perplexing for developers to comprehend. This research postulates that tools can computationally expose their internal reasoning processes to generate assistive error explanations that more closely align with how developers explain errors to themselves. CCS Concepts •Human-centered computing → Human computer interaction (HCI); •Software and its engineering → Software usability; •Theory of computation → Program analysis;

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Section: Resultsmentioning

confidence: 99%

How should static analysis tools explain anomalies to developers?

Barik

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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“…Semi-automic diagnosis [14,54,148,149,189,193] Feedback-based [114,153] Checklists [8,140] UI & navigation tools [5,21,33,70,79,138] Alarm-relevant queries [42,80,127,190] Automated repair [12,15,115,116,179] Others [6,112,119,121,133,136,144] Fig. 1.…”

Section: Overview Of the Extracted Datamentioning

confidence: 99%

Survey of Approaches for Postprocessing of Static Analysis Alarms

Muske

Serebrenik

2022

ACM Comput. Surv.

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Static analysis tools have showcased their importance and usefulness in automated detection of defects. However, the tools are known to generate a large number of alarms which are warning messages to the user. The large number of alarms and cost incurred by their manual inspection have been identified as two major reasons for underuse of the tools in practice. To address these concerns plentitude of studies propose postprocessing of alarms: processing the alarms after they are generated. These studies differ greatly in their approaches to postprocess alarms. A comprehensive overview of the postprocessing approaches is, however, missing. In this article, we review 130 primary studies that propose postprocessing of alarms. The studies are collected by combining keywords-based database search and snowballing. We categorize approaches proposed by the collected studies into six main categories: clustering, ranking, pruning, automated elimination of false positives, combination of static and dynamic analyses, and simplification of manual inspection. We provide overview of the categories and sub-categories identified for them, their merits and shortcomings, and different techniques used to implement the approaches. Furthermore, we provide (1) guidelines for selection of the postprocessing techniques by the users/designers of static analysis tools; and (2) directions that can be explored by the researchers.

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“…Static-analysis-based patching. Some tools like FindBugs and Clang provide "quick fix" suggestions to help developers fix certain classes of bugs [3], as do IDEs like IntelliJ [19], Eclipse [18], and Visual Studio [35]. However, these recommendations are instantiated from statically defined bug-fix strategies, while Phoenix automatically learns these strategies from examples.…”

Section: Related Workmentioning

confidence: 99%

Phoenix: automated data-driven synthesis of repairs for static analysis violations

Bavishi

Yoshida

Prasad

2019

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

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Traditional automatic program repair (APR) tools rely on a testsuite as a repair specification. But test suites even when available are not of specification quality, limiting the performance and hence viability of test-suite based repair. On the other hand, static analysisbased bug finding tools are seeing increasing adoption in industry but still face challenges since the reported violations are viewed as not easily actionable. We propose a novel solution that solves both these challenges through a technique for automatically generating high-quality patches for static analysis violations by learning from examples. Our approach uses the static analyzer as an oracle and does not require a test suite. We realize our solution in a system, Phoenix, that implements a fully-automated pipeline that mines and cleans patches for static analysis violations from the wild, learns generalized executable repair strategies as programs in a novel Domain Specific Language (DSL), and then instantiates concrete repairs from them on new unseen violations. Using Phoenix we mine a corpus of 5,389 unique violations and patches from 517 Github projects. In a cross-validation study on this corpus Phoenix successfully produced 4,596 bug-fixes, with a recall of 85% and a precision of 54%. When applied to the latest revisions of a further 5 Github projects, Phoenix produced 94 correct patches to previously unknown bugs, 19 of which have already been accepted and merged by the development teams. To the best of our knowledge this constitutes, by far the largest application of any automatic patch generation technology to large-scale real-world systems. CCS CONCEPTS • Software and its engineering → Automated static analysis; Domain specific languages; Programming by example; Software testing and debugging.

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From Quick Fixes to Slow Fixes: Reimagining Static Analysis Resolutions to Enable Design Space Exploration

Cited by 20 publications

References 22 publications

How should static analysis tools explain anomalies to developers?

How should static analysis tools explain anomalies to developers?

Survey of Approaches for Postprocessing of Static Analysis Alarms

Phoenix: automated data-driven synthesis of repairs for static analysis violations

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