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SFLKit: A Workbench for Statistical Fault Localization

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conference contribution
posted on 2023-11-29, 18:22 authored by Marius SmytzekMarius Smytzek, Andreas ZellerAndreas Zeller
Statistical fault localization aims at detecting execution features that correlate with failures, such as whether individual lines are part of the execution. We introduce SFLKit, an out-of-the-box workbench for statistical fault localization. The framework provides straight- forward access to the fundamental concepts of statistical fault lo- calization. It supports five predicate types, four coverage-inspired spectra, like lines, and 38 similarity coefficients, e.g., TARANTULA or OCHIAI, for statistical program analysis. SFLKit separates the execution of tests from the analysis of the re- sults and is therefore independent of the used testing framework. It leverages program instrumentation to enable the logging of events and derives the predicates and spectra from these logs. This instru- mentation allows for introducing multiple programming languages and the extension of new concepts in statistical fault localization. Currently, SFLKit supports the instrumentation of python programs. SFLKit is highly configurable, requiring only the logging of the re- quired events.

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Preferred Citation

Marius Smytzek and Andreas Zeller. SFLKit: A Workbench for Statistical Fault Localization. In: European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE). 2022.

Primary Research Area

  • Secure Connected and Mobile Systems

Name of Conference

European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)

Legacy Posted Date

2022-09-02

Open Access Type

  • Unknown

BibTeX

@inproceedings{cispa_all_3759, title = "SFLKit: A Workbench for Statistical Fault Localization", author = "Smytzek, Marius and Zeller, Andreas", booktitle="{European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE)}", year="2022", }

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