TY  - JOUR
T1  - Efficient Mutation-based Fault Localization using Predictive Mutation Analysis
AU  - Kim, Yunho 
AU  - Jung, Namhoon 
AU  - Lee, Insub 
AU  - Nam, Hyoju 
AU  - Cho, Kyutae 
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.11.915
KW  - Sangeun Chae
KW  - Beomsuk Nam
AB  - 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.