IMPROVING DETECTION PERFORMANCE OF DUPLICATE BUG REPORTS USING EXTENDED CLASS CENTROID INFORMATION
Abstract
In software maintenance, bug reports play an important role in the correctness of software packages. Unfortunately, the duplicatebug report problem arises because there are too many duplicate bug reports in various software projects. Handling with duplicate bug reports is thus time-consuming and has high cost of software maintenance. Therefore, this research introduces a detection scheme based on the extended class centroid information (ECCI) to enhance the
detection performance. This method is extended from the previous one, which used only centroid method without considering the effects of both inner and inter class. Besides, this method also improved the previous use of normalized cosine in identifying the similarity between two bug reports by denormalized cosine. The effectiveness of ECCI is proved through the empirical study with three open-source projects: SVN, Argo UML and Apache. The experimental results show that
ECCI outperforms other detection schemes by about 10% in all cases.
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