A Novel Approach on Effective Bug Assortment Using Data Classification Techniques

Abstract

Most of the software companies needs to deal with large number of software bugs every day. Software bugs are unavoidable and fixing software bugs is an expensive task. The goal of effective bug triaging software is to assign potentially experienced developers to new-coming bug reports. To reduce time and cost of bug triaging, an automatic approach is proposed in this paper that predicts a developer with relevant experience to solve or fix the new coming bug report. In this paper, the five term selection methods on the accuracy of bug assignment are used. In addition, the load between developers based on their experience is re-balanced. The proposed system is built with intention to suggest or recommend the bug and not to automatically assign it. This allows a window to handle real time crisis that come up during project development lifecycle.

Keywords— Mining software repositories; application of data pre-processing; data management in bug repositories; bug data reduction; feature selection; instance selection; bug triage.