An Appropriate Method Ranking Approach for Localizing Bugs using Minimized Search Space

In automatic software bug localization, source code analysis is usually used to localize the buggy code without manual intervention. However, due to considering irrelevant source code, localization accuracy may get biased. In this paper, a Method level Bug localization using Minimized search space (MBuM) is proposed for improving the accuracy, which considers only the liable source code for generating a bug. The relevant search space for a bug is extracted using the execution trace of the source code. By processing these relevant source code and the bug report, code and bug corpora are generated. Afterwards, MBuM ranks the source code methods based on the textual similarity between the bug and code corpora. To do so, modified Vector Space Model (mVSM) is used which incorporates the size of a method with Vector Space Model. Rigorous experimental analysis using different case studies are conducted on two large scale open source projects namely Eclipse and Mozilla. Experiments show that MBuM outperforms existing bug localization techniques.

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