A FAULT LOCALIZATION FRAMEWORK TO ALLEVIATE THE IMPACT OF EXECUTION SIMILARITY

Abstract

Coverage-based fault localization (CBFL) techniques contrast the execution spectra of a program entity to assess the extent of how much a program entity is being related to faults. However, different test cases may result in similar executions, which further make the execution spectra of program entities be indistinguishable among similar executions. As a consequence, most of the current CBFL techniques are impacted by the noise of indistinguishable spectra. To alleviate the impact of execution similarity and improve the effectiveness of CBFL techniques, we propose a general fault localization framework. This framework is general to current execution spectra based CBFL techniques, which could synthesize a fault localization technique based on a given base technique. To synthesize the new technique, we use the concept of coverage vector to model execution spectra and capture the execution similarity, then reduce the impact of execution similarity by counting distinct coverage vectors, and finally assess the suspiciousness of basic blocks being related to faults with the spectra of distinct coverage vectors. We adopt four representative fault localization techniques as base techniques, use seven Siemens programs and three median-sized real-life UNIX utility programs as subject programs, to conduct an experimental study on the effectiveness of our framework. The empirical evaluation shows that our framework can effectively alleviate the impact of execution similarity and generate more effective fault localization techniques based on existing ones.