Suspicious Types and Bad Neighborhoods

Abstract

Spectrum-based fault localization and its formulas often struggle with large spectra containing many expressions irrelevant to the fault, which impacts its overall effectiveness. Spectra can inflate for large programs or on finer granularity, such as expression-level coverage from other languages like Haskell. To address this, we introduce 25 rules to filter the spectra based on type information, AST attributes, and test results. These aim to reduce the suspiciousness of innocent locations (bug-free expressions) and improve the performance of SBFL formulas w.r.t. TOP50 and TOP100 metrics. Our experiment, conducted on 11 Haskell programs, shows that individual filters significantly reduce spectra size, although some data points (faulty expressions) become unsolvable. By applying established SBFL formulas like Ochiai and Tarantula to these reduced spectra, we observe average improvements of up to 40% w.r.t. TOP50 for individual soft rules, such as proximity to failure. Combining the best-performing filters yields improvements of 45.5% for Ochiai, 67.4% for DStar2, and 45.5% for Tarantula. The most effective filtering rules over all formulas captured proximity to failing expressions, usage of a non-unique type, and whether a failing test covered the expression. Our results suggest that simple, straightforward filters can produce substantial performance gains. We further identify 4 uncovered bugs originating from code generation (common in functional programming) and system tests, which can not be addressed purely by spectrumbased fault localization.