SFLKit: A Workbench for Statistical Fault Localization

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


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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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