Hybrid-MUSE: Mutating Faulty Programs For Precise Fault Localization

Abstract—This paper presents Hybrid-MUSE, a new fault localization technique that combines MUtation-baSEd fault local-ization (MUSE) and Spectrum-Based Fault Localization (SBFL) technique. The core component of Hybrid-MUSE, MUSE, iden-tifies a faulty statement by utilizing different characteristics of two groups of mutants – one that mutates a faulty statement and the other that mutates a correct statement. This paper also proposes a new evaluation metric for fault localization techniques based on information theory, called Locality Information Loss (LIL): it can measure the aptitude of a localization technique for automated fault repair systems as well as human debuggers. The empirical evaluation using 51 faulty versions of the five real-world programs shows that Hybrid-MUSE outperforms the state-of-art fault localization technique significantly. For example, Hybrid-MUSE localizes a fault after reviewing 1.65 % of executed statements on average, which is around 5.6 times more precise than the state-of-the-art SBFL technique, Op2. I