SusRec: An Approach to Sustainable Developer Recommendation for Bug Resolution Using Multimodal Ensemble Learning

Abstract

The sustainability of an open source project is essential for the long-term and reliable development of software. Most existing studies focus on the recommendation accuracy of bug report assignment while ignoring inexperienced developers in the open source community. This gives inexperienced developers less opportunity to resolve bugs and can cause them to gradually lose interest in the development of open source software (OSS). To address this problem, this article proposes a novel approach called sustainable recommender (SusRec) to make sustainable report assignments without sacrificing accuracy. The SusRec approach is based on multimodal learning and ensemble learning, and it consists of two stages: the preprocessing stage and the developer scoring stage. In the preprocessing stage, the approach selects candidate developers who have participated in the resolution of bugs under the product of a new bug report. It then divides the candidate developers into three types—core developers, active developers, and peripheral developers—according to their experience. In the developer scoring stage, multimodal learning is adopted to score the three types of bug report–developer pairs, and ensemble learning is adopted to weight the scores of the three types of bug report–developer pairs and recommend developers for bug reports. We conduct extensive experiments using the bug repositories of the Eclipse and Mozilla projects to compare the proposed SusRec approach with the baseline methods in bug report assignment. The results demonstrate that the proposed SusRec approach cannot only improve the accuracy of developer recommendations for bug reports, but also the sustainability of OSS projects by providing more opportunities for active developers and peripheral developers to participate in bug resolution.