IMPROVING DETECTION PERFORMANCE OF DUPLICATE BUG REPORTS USING EXTENDED CLASS CENTROID INFORMATION

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 thedetection 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 thatECCI outperforms other detection schemes by about 10% in all cases.

MULTI-FEATURE MODEL FOR DUPLICATE BUG REPORTS DETECTION SOLUTION

For open source software such as Firefox, Eclipse, Subversion,. . . they usually have a system for bug management that sent by users. These bug reports help the system identify various software bugs which makes software maintenance better. However, a situation occurs that there are many error reports sent to the processing repository that these bug reports were previously reported by different users, this is called duplicate bug reports. In this paper, we introduce a multi-feature model combined with weighted improvements from CFC (ClassFeature-Centroid) to detect duplicate bug reportsautomatically. We have experimented on three projects of Mozilla, Eclipse and OpenOffice. The results show that our method can improve 8-12% better as compared to the compared methods.