Effective Bug Triage for Software Development and Maintenance

Abstract

Software businesses allocate about 45% of their budget to resolving issues. Bug triage is an essential step in the bug-fixing process that aims to effectively provide a developer with information about a new bug. This research focusses on the issue of data minimization in bug triage, which involves reducing and enhancing the quality of bug data. Utilize instance and feature selection techniques to simultaneously decrease the size of both the word and data dimensions related to bugs. The objective is to construct a prediction model for a novel bug data set by utilizing qualities from previous bug data sets. Additionally, we aim to assess the comparative significance of employing feature and instance selection in the sequence in which they are implemented. Empirically evaluate the effectiveness of data reduction by analyzing a total of 600,000 bug reports from two significant open-source projects, Mozilla and Eclipse. The findings indicate that our data reduction technique has the potential to effectively decrease the bulk of data while enhancing bug triage accuracy. Our study effort presents a methodology for utilizing data processing techniques to provide superior, sparsely populated bug data for the sake of software development and maintenance.