Abstract
Software fault localization has attracted a lot of attention recently. Most existing methods focus on finding a single suspicious statement of code which is likelihood of containing bugs. Despite the accuracy of such methods, developers have trouble understanding the context of the bug, given each bug location in isolation.
There is a high possibility of locating bug contexts through finding discriminative execution sub-paths between failing and passing executions. Representing each execution of a program as a graph, discriminative sub-paths could be identified by applying a graph mining algorithm. These sub-paths may help the debugger to easily identify the major causes of faults and its infection flow through the program. In this paper, a novel approach to mine discriminative sub-graphs as indicators of program faults is proposed. We formulate an efficient function to find most discriminative patterns between weighted failing and passing graphs. Experimental results indicate that significant improvement in precision of bug localization is achieved using the proposed discriminative sub-graph mining approach.
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Parsa, S., Naree, S.A., Koopaei, N.E. (2011). Software Fault Localization via Mining Execution Graphs. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21887-3_46
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DOI: https://doi.org/10.1007/978-3-642-21887-3_46
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