Empirical Investigations of More Practical Fault Localization Approaches

Developers often spend much of their valuable development time on software debugging and bug finding. In addition, software defects cost software industry as a whole hundreds or even a trillion of US dollars. As a result, many fault localization (FL) techniques for localizing bugs automatically, have been proposed. Despite its popularity, adopting FL in industrial environments has been impractical due to its undesirable accuracy and high runtime overhead cost. Motivated by the real-world challenges of FL applicability, this dissertation addresses these issues by proposing two main enhancements to the existing FL. First, it explores different strategies to combine a variety of program execution information with Information Retrieval-based fault localization (IRFL) techniques to increase FL's accuracy. Second, this dissertation research invents and experiments with the unconventional techniques of Instant Fault Localization (IFL) using the innovative concept of triggering modes. Our empirical evaluations of the proposed approaches on various types of bugs in a real software development environment shows that both FL's accuracy is increased and runtime is reduced significantly. We find that execution information helps increase IRFL's Top-10 by 17–33% at the class level, and 62–100% at the method level. Another finding is that IFL achieves as much as 100% runtime cost reduction while gaining comparable or better accuracy. For example, on single-location bugs, IFL scores 73% MAP, compared with 56% of the conventional approach. For multi-location bugs, IFL's Top-1 performance on real bugs is 22%, just right below 24% that of the existing FL approaches. We hope the results and findings from this dissertation help make the adaptation of FL in the real-world industry more practical and prevalent.