How do Execution Features Improve Statistical Fault Localization? An Empirical Study

Abstract

Automated fault localization helps developers find faults in large code bases. Statistical fault localization (SFL) ranks suspicious lines from pass/fail spectra, but line execution alone misses information like data-flow, values, or branch conditions that explain why a failure occurs. This study evaluates whether augmenting SFL with execution features improves localization accuracy and developer-oriented inspection effort. We extract execution features with EFDD for all Tests4Py subjects, train per-subject random forests, map importances to source lines, and combine the resulting weights with established SFL formulas. The evaluation measures reference-patch accuracy, line- and function-level effort, robustness, and feasibility using a confounder-adjusted mixed-effects model, corroborated by paired statistical tests and outcome-neutral quality checks.