Abstract
Spectrum-based Fault Localization (SBFL) has been proven to be an effective technique to locate faulty statement in program code. SBFL metrics exploit the records of statement execution (spectra) by pass and fail test cases for each statement to rank its likeliness to be faulty. However, in some cases, these spectra contain duplicated and ambiguous information or noise which may deteriorate the performance of SBFL metrics. We propose six noise reduction schemes to eliminate test cases which provide duplicated and ambiguous information and evaluate the resulting performance improvements in SBFL metrics. Based on our findings, we further provide a guide for SBFL practitioners to select the best performing noise reduction scheme for the SBFL metrics that they use.
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