SGS: Mutant Reduction for Higher-order Mutation-based Fault Localization.

MBFL (Mutation-Based Fault Localization) is one of the most commonly studied fault localization techniques due to its promising fault localization effectiveness. However, MBFL incurs a high execution cost as it needs to execute the test suite on a large number of mutants. While previous studies have proposed mutant reduction methods for FOMs (First-Order Mutants) to help alleviate the cost of MBFL, the reduction of HOMs (Higher-Order Mutants) has not been thoroughly investigated. In this study, we propose SGS (Statement Granularity Sampling), a method which conducts HOMs reduction for HMBFL (Higher-Order Mutation-Based Fault Localization). Considering the relationship between HOMs and statements, we sample HOMs at the statement level to ensure each statement has corresponding HOMs. We empirically evaluate the fault localization effectiveness of HMBFL using SGS on 237 multiple-fault programs taken from the SIR and Codeflaws benchmarks. The experimental results show that (1) The best sampling ratio for HMBFL with SGS is 20%, which preserves the performance and reduces execution costs by 80%; (2) The fault localization accuracy of HMBFL with SGS outperforms the state-of-the-art SBFL (Spectrum-Based Fault Localization) and MBFL techniques by 20%.