MFL: Method-Level Fault Localization with Causal Inference
Gang Shu, Boya Sun, Andy Podgurski, Feng Cao · 2013
Recent studies have shown that use of causal inference techniques for reducing confounding bias improves the effectiveness of statistical fault localization (SFL) at the level of program statements. However, with very large programs and test suites, the overhead of statement-level causal SFL may be excessive. Moreover cost evaluations of statement-level SFL techniques generally are based on a questionable assumption-that software developers can consistently recognize faults when examining statements in isolation. To address these issues, we propose and evaluate a novel method-level SFL technique called MFL, which is based on causal inference methodology. In addition to reframing SFL at the method level, our technique incorporates a new algorithm for selecting covariates to use in adjusting for confounding bias. This algorithm attempts to ensure that such covariates satisfy the conditional exchangeability and positivity properties required for identifying causal effects with observational data. We present empirical results indicating that our approach is more effective than four method-level versions of well-known SFL techniques and that our confounder selection algorithm is superior to two alternatives.