TL;DR
This paper introduces S-DABT, a comprehensive bug triage method that uses integer programming and machine learning to assign bugs efficiently by considering textual data, bug dependencies, and developer schedules, reducing bug fixing times.
Contribution
The novel approach integrates developer schedules, bug dependencies, and textual data into a unified bug triage model, improving assignment accuracy and reducing fixing times.
Findings
Decreases average bug fixing times in open-source projects.
Improves developer utilization and task distribution.
Reduces bug dependency graph complexity and infeasible assignments.
Abstract
Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and machine learning techniques to assign bugs to suitable developers. Unlike prior works that largely focus on a single component of the bug reports, our approach takes into account the textual data, bug fixing costs, and bug dependencies. We further incorporate the schedule of developers in our formulation to have a more comprehensive model for this multifaceted problem. As a result, this complete formulation considers developers' schedules and the blocking effects of the bugs while covering the most significant aspects of the previously proposed methods. Our numerical study…
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