Optimization of Bug Reporting System (BRS): Artificial Intelligence Based Method to Handle Duplicate Bug Report

Abstract

Bug tracking and reporting are some of the most critical activities/steps in software engineering and implementation which has a direct impact on the quality of tested software and productivity of resources allocated to that software. Bug Reporting System (BRS) plays an important role in tracking all essential bug reports during software development life cycle (SDLC). Duplicate Bug Reports (DBR) have an adverse effect on the software quality assurance process as it enhances the processing time of bug triager whose job is to keep track of all bug reports and also on application developers, to whom bug tickets are assigned by the triager. Duplicate bug reports if remain unidentified may result in enhancing bug handling time (rework) and decreasing overall team performance. However identification of duplicate bug report remains as a critical task as it is a tough job to manually identify all second images of earlier reported bug. In this paper we have proposed an enhancement in existing BRS which uses artificial intelligence based intelligent techniques to detect the existence of a duplicate bug. Every new bug reported to the system will be marked with an identification tag. A bug containing duplicate tag will be phased out from the bug repository which will not only reduce the additional effort on bug triage but also improve the system’s performance.