Holmes: Effective Statistical Debugging via Efficient Path Profiling

Statistical debugging aims to automate the process of isolating bugs by profiling several runs of the program and using statistical analysis to pinpoint the likely causes of failure. In this paper, we investigate the impact of using richer program profiles such as path profiles on the effectiveness of bug isolation. We describe a statistical debugging tool called Holmes that isolates bugs by finding paths that correlate with failure. We also present an adaptive version of Holmes that uses iterative, bug-directed profiling to lower execution time and space overheads. We evaluate Holmes using programs from the SIR benchmark suite and some large real world applications. Our results indicate that path profiles can help isolate bugs more precisely by providing more information about the context in which bugs occur. Moreover, bug-directed profiling can efficiently isolate bugs with low overheads, providing a scalable and accurate alternative to sparse random sampling.