Fault Localization via Mining Program Dynamic Execution Graph ⋆

This paper is concerned with the problem of locating the code area related to software potential fault quickly and accurately in software testing period. A new method Sig BB based on graph model is proposed for mining the suspicious fault nodes from the passing and failing execution graphs. Representing each execution of a program as a graph, the graphs are divided into the passing and failing sets. By extracting the most representative passing and failing graphs based on these sets, the discriminative sub-graph is mined between the two representative graphs. First, Sig BB searches the max common graph, and then gets the opposite nodes set. The discriminative sub-graph is obtained by organizing and extending the set finally. Since the detected code scale is associated with the sorting of suspicious nodes, a suspicious metric strategy is also designed to sort the nodes in the discriminative sub-graph. Experimental results indicate that our method is both effective and efficient for software fault localization.