Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory–Attention Mechanism - Geunseok Yang; Jinfeng Ji; Jaehee Kim (Applied Sciences)
The rapid expansion of software applications has led to an increase in the frequency of bugs, which are typically reported through user-submitted bug reports. Developers prioritize these reports based on severity and project schedules. However, the manual process of assigning bug priorities is time-consuming and prone to inconsistencies. To address these limitations, this study presents a Priority-Sensitive LSTM–Attention mechanism for automating bug priority prediction. The proposed approach extracts features such as product and component details from bug repositories and preprocesses the data to ensure consistency. Priority-based feature selection is applied to align the input data with the task of bug prioritization. These features are processed through a Long Short-Term Memory (LSTM) network to capture sequential dependencies, and the outputs are further refined using an Attention mechanism to focus on the most relevant information for prediction. The effectiveness of the proposed model was evaluated using datasets from the Eclipse and Mozilla open-source projects. Compared to baseline models such as Naïve Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. It recorded an accuracy of 93.00% for Eclipse and 84.11% for Mozilla, representing improvements of 31.11% and 40.39%, respectively, over the baseline models. Statistical verification confirmed that these performance gains were significant. This study distinguishes itself by integrating priority-based feature selection with a hybrid LSTM–Attention architecture, which enhances prediction accuracy and robustness compared to existing methods. The results demonstrate the potential of this approach to streamline bug prioritization, improve project management efficiency, and assist developers in resolving high-priority issues.