Scalable graph analyzing approach for software fault-localization

In this paper, a new approach for analyzing program behavioral graphs to detect fault relevant paths is presented. The existing graph mining approaches for bug localization merely detect discriminative sub-graphs between failing and passing runs. However, they are not applicable when the context of a failure is not appeared in a discriminative pattern. In our proposed method, the suspicious transitions are identified by contrasting nearest neighbor failing and passing dynamic behavioral graphs. For finding similar failing and passing graphs, we first convert the graphs into adequate vectors. Then, a combination of Jacard-Cosine similarity measures is applied to identify the nearest graphs. The new scoring formula takes advantage of null hypothesis testing for ranking weighted transitions. The main advantage of the proposed technique is its scalability which makes it work on large and complex programs with huge number of predicates. Another main capability of our approach is providing the faulty paths constructed from fault suspicious transitions. Considering the weighted execution graphs in the analysis enables us to find those types of bugs which reveal themselves in specific number of transitions between two particular predicates. The experimental results on Siemens test suite and Space program manifest the effectiveness of the proposed method on weighted execution graphs for locating bugs in comparison with other methods.