Yunho Kim  Namhoon Jung  Insub Lee  Hyoju Nam  Kyutae Cho

10.5626/JOK.2025.52.11.915

Sangeun Chae Beomsuk Nam

One of the most challenging problems in software debugging is localizing the faulty code elements that cause errors. Mutation-based fault localization techniques, which employ mutation analysis, can accurately identify these faulty elements but are often impractical due to the significant time required for mutation analysis. This paper proposes an efficient mutation-based fault localization technique that utilizes predictive mutation analysis. Instead of conducting the time-consuming mutation analysis for every debugging attempt, the proposed approach trains a machine learning model using existing mutation analysis results. This model then predicts the outcomes of further mutation analyses, enhancing the efficiency of fault localization. Experimental results using the SIR benchmark demonstrate that the proposed method can accurately localize faulty code elements while requiring less time than existing mutation-based fault localization techniques.