Enhancing Bug Assignment with Developer-Specific Feature Extraction and Hybrid Deep Learning
Geunseok Yang, Jinfeng Ji, Dongkyu Kim · Electronics · 2025
The increasing reliance on software in diverse domains has led to a surge in user-reported functional enhancements and unexpected bugs. In large-scale open-source projects like Eclipse and Mozilla, initial bug assignment frequently faces challenges, with approximately 50% of bug reports being reassigned due to the inability of the initially assigned developer to resolve the issue effectively. This reassignment process contributes to elevated software maintenance costs and delays in bug resolution. To address this, we propose a developer recommendation model that assigns the most suitable developer for a given bug report at the outset, thereby minimizing reassignment rates. Our approach combines a top-K feature selection algorithm tailored for each developer with a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to capture the nuanced patterns in bug reports and developer expertise. The model was evaluated on prominent open-source projects, including Google Chrome, Mozilla Core, and Mozilla Firefox. Experimental results show that the proposed model significantly outperforms baseline approaches, with an improvement in developer recommendation accuracy of approximately 0.3582 when comparing the best-performing configuration to the worst-performing configuration of our model. Furthermore, the baseline difference was reduced by approximately 0.1343. A statistical analysis confirms the significant performance improvement achieved by the proposed method over existing baselines. These findings underscore the potential of our model to enhance efficiency in bug resolution workflows, reduce maintenance costs, and improve overall software quality in open-source ecosystems.