The paper “Augmenting Automated Spectrum Based Fault Localization For MultipleFaults”, co-authored by LASIGE’s research member José Campos has been published at the 32nd International Joint Conference on Artificial Intelligence (IJCAI), a core A* conference.

Spectrum-based Fault Localization (SBFL) uses the coverage of test cases and their outcome (pass/fail) to predict the “suspiciousness” of program components, e.g., lines of code.  SBFL is, perhaps, the most successful fault localization technique due to its simplicity and scalability.  However, SBFL heuristics do not perform well in scenarios where a program may have multiple faulty components. Thus, this paper proposes a new algorithm that “augments” previously proposed SBFL heuristics to produce a ranked list where faulty components ranked low by base SBFL metrics are ranked significantly higher.  The algorithm attempts to “bubble up” faulty components which are ranked lower by base SBFL metrics.  We compare our algorithm to the most popular SBFL metrics and demonstrate statistically significant improvement in the developer effort for fault localization with respect to the basic strategies.

A pre-print version of the paper is available: here.