Simultaneous Localization of Software Faults Based on Complex Network Theory

Effective debugging is necessary for producing high quality and reliable software. Fault localization plays a vital role in the debugging process. However, fault localization is the most tedious and expensive activity in program debugging, as such, effective fault localization techniques that can identify the exact location of faults is eminent. Despite various fault localization techniques proposed, their application in multiple-fault programs is limited. The presence of multiple faults in a program reduces the efficacy of the existing fault localization techniques to locate faults effectively due to fault interference. Moreover, most of these techniques are unable to localize faults simultaneously. This has led researchers to adopt alternative approaches, such as one-fault-at-a-time debugging and parallel debugging. In this paper, we propose a novel fault localization technique based on complex network theory (FLCN) to improve localization effectiveness in programs with single-fault and multiple-fault and to aid developers to localize multiple faults simultaneously in a single diagnosis rank list. The proposed technique ranks faulty statements based on their behavioral abnormalities and distance between faulty statements in both passed and failed test executions. Two graph-based centrality measures are adopted for fault diagnosis, namely, degree centrality and closeness centrality and a new ranking formula is proposed. FLCN is evaluated across 14 subjects with both single-fault and multiple-fault programs. Our experimental results show that FLCN is more effective at locating faults when compared with existing state-of-the-art fault localization techniques with improvement of fault localization effectiveness in both single-fault and multiple-fault programs.