SFLKit: a workbench for statistical fault localization

Abstract

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 straightforward access to the fundamental concepts of statistical fault localization. It supports five predicate types, four coverage-inspired spectra, like lines, and 44 similarity coefficients, e.g., TARANTULA or OCHIAI, for statistical program analysis.