Behavioral Fault Localization by Sampling Suspicious Dynamic Control Flow Subgraphs

Abstract

We present a new algorithm, Score Weighted Random Walks (SWRW), for behavioral fault localization. Behavioral fault localization localizes faults (bugs) in programs to a group of interacting program elements such as basic blocks or functions. SWRW samples suspicious (or discriminative) subgraphs from basic-block level dynamic control flow graphs collected during the execution of passing and failing tests. The suspiciousness of a subgraph may be measured by any one of a family of new metrics adapted from probabilistic formulations of existing coverage-based statistical fault localization metrics. We conducted an empirical evaluation of SWRW with nine subgraph-suspiciousness measures on five real-world subject programs. The results indicate that SWRW outperforms previous fault localization techniques based on discriminative subgraph mining.