Denoising Fault Localization with Test Line Proximity

When a program fails, statistical fault localization (SFL) provides important debugging hints by identifying the locations whose execution most correlates with failures. However, such correlations can be weakened if a test contains both _passing_ and _failing_ assertions, creating ambiguous and misleading associations. Likewise, if multiple lines correlate with the same strength, SFL provides little guidance to disambiguate between them.

This paper proposes a novel proximity-based weighting scheme for SFL that assigns different _weights_ to locations in the test subject based on temporal proximity to a failure. The more recently a subject line is executed before the test fails, the higher its weight. We operationalize a known heuristic into a lightweight statistical form compatible with existing SFL formulas. Our approach applies to _any test_, from simple single-line tests (where it preserves SFL behavior), to single-assertion tests with multiple lines (where it benefits from temporal proximity), to complex multi-assertion tests (where it provides the most benefit by distinguishing failing from passing assertions). Once computed, the weights can be integrated into any existing SFL technique.

Our evaluation of proximity-weighted fault localization on 310~real-world programs shows that it consistently outperforms fault localization techniques across all test types. Proximity-weighted fault localization shows per-subject relative improvements of 200%–400%, meaning that, for a typical subject, it provides 3 to 5 times the baseline effectiveness. These improvements represent substantial gains over baseline techniques. Our approach can be integrated into existing fault localization techniques to improve performance, making it a valuable addition to automated debugging.