Fault localization based on weighted software behavior graph mining

The result of most fault localization approaches are given in the form of suspicious statement sequence ranking in descending order. However, examining a suspicious statement in isolation is hard for developers to understand the cause of failure, often they need more context information associated with the error. To solve this problem, this paper proposed a weighted software behavior graph to represent the path of passed execution or failed execution. It uses the execution frequency as weights, so that it can better handle loops, recursive and other structures than LEAP. Then, branch and bound search graph mining algorithm is performed on the weighted software behavior graphs to identify differences between passed and failed weighted software behavior graph as a bug signature. The proposed approach can not only localize the fault but also provide the failure context to facilitate fault comprehension. Experimental results on Siemens and Flex showed that the proposed approach can localize more faults than Tarantula and LEAP, when examining the same percent of code. In particular, the proposed approach has higher fault localization accuracy, especially in dealing with redundant code, missing codes, variable substitution errors, and errors that will directly change the execution path. © 2016, Science Press. All right reserved.