A Search Based Context-Aware Approach for Understanding and Localizing the Fault via Weighted Call Graph

Strictly speaking, fault localization includes assessing the code risk of being faulty and identifying the real fault. In practice, only highlighting some possible faulty statements is not helpful enough to reason the roots of the observed failures in a system. Programmers need to manually inspect the highlighted risky statements one by one, reading and understanding their contexts, in order to identify the real faulty ones. However, most related works have been focusing on risk assessment by simply ignoring the fault identification, which makes such techniques much less practical in real world. Therefore, in this paper, we propose a context-aware approach to assist fault comprehension and identification. Built on risk assessment results, our approach searches for the faults on Weighted Call Graph. In our approach the risky statements are re-ordered by function call chains, which can provide much richer information to understand the context and hence reduce the efforts in manual code inspection. Case studies with three open-source systems show that the proposed approach could help to improve the effectiveness of the whole fault localization process.