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Improved Software Fault Detection with Graph Mining

Improved Software Fault Detection with Graph Mining

Frank Eichinger, Klemens Böhm, Matthias Huber


Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki, Finland, 2008

This work addresses the problem of discovering bugs in software development. We investigate the utilisation of call graphs of program executions and graph mining algorithms to approach this problem. We propose a novel reduction technique for call graphs which introduces edge weights. Then, we present an analysis technique for such weighted call graphs based on graph mining and on traditional feature selection. Our new approach finds bugs which could not be detected so far. With regard to bugs which can already be localised, our technique also doubles the precision of finding them.


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