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.