{"ID":5346763,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T13:56:16.32655622Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30324","arxiv_id":"2606.30324","title":"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.","short_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 augm...","url_abs":"https://arxiv.org/abs/2606.30324","url_pdf":"https://arxiv.org/pdf/2606.30324v1","authors":"[\"Marius Smytzek\",\"Andreas Zeller\"]","published":"2026-06-29T14:07:47Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}