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

2026-06-29

AI Summary

This study addresses the limitations of traditional statistical fault localization (SFL), which relies solely on code execution traces and often fails to accurately pinpoint root causes. To overcome this, the authors systematically incorporate execution features—such as data flow, variable values, and branch conditions—extracted via the EFDD tool from the Tests4Py dataset. They train project-specific random forest models and map feature importance back to source code lines, integrating these insights with classical SFL formulas to enhance localization accuracy. Rigorous evaluation is conducted using a confounder-adjusted mixed-effects model and paired statistical tests. Experimental results demonstrate that the proposed approach significantly improves the accuracy of reference patches while reducing inspection effort at both line and function levels, confirming its robustness and practicality across multiple dimensions.

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.

Problem

• Statistical Fault Localization
• Execution Features
• Fault Localization Accuracy
• Developer Inspection Effort
• Program Spectrum

Innovation

• execution features
• statistical fault localization
• random forests
• fault localization accuracy
• developer inspection effort