# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 7 Conclusions  
This study addressed the challenge of bug priority prediction by proposing a Priority-Sensitive LSTM-Attention mechanism, aimed at automating and improving the accuracy of bug prioritization. Automated prioritization provides a significant advantage to developers by enabling them to manage their workloads efficiently and effectively. Furthermore, it aids project managers in streamlining project schedules and resource allocation by reducing reliance on subjective, manual priority assignment.  
The proposed approach integrates priority-based feature selection and a hybrid LSTM-Attention mechanism. Features were extracted based on the product and component attributes of bug reports, emphasizing priority-specific data. These features were then processed using the LSTM algorithm to capture sequential dependencies, with the outputs fed into an Attention mechanism to highlight the most relevant information for predicting bug priorities.  
The model's performance was rigorously evaluated using the Eclipse and Mozilla datasets and benchmarked against several baseline methods, including traditional machine learning and deep learning models. The results showed that the proposed model significantly outperformed the baselines, achieving accuracies of 84.11% for Eclipse and 93.00% for Mozilla. The comparative analysis revealed that the proposed model improved priority prediction accuracy by approximately 31.11% for Eclipse and 40.39% for Mozilla, reflecting a substantial advancement in bug priority prediction.  
Additionally, statistical verification confirmed the robustness of the proposed model, with significant differences observed between the proposed method and the baseline models. These findings underscore the reliability and scalability of the model, particularly for large-scale and complex datasets.  
While the proposed model showed a strong performance, there are opportunities for further improvement. Future research should focus on validating the model across a broader range of datasets, including other open-source projects and enterprise-level software. Addressing issues such as data imbalance and variability in bug report structures could further enhance the model's generalizability. Moreover, incorporating advanced data augmentation techniques and exploring alternative architectures could lead to additional performance gains.  
In conclusion, the Priority-Sensitive LSTM-Attention mechanism represents a significant step forward in automating bug prioritization. Its ability to deliver accurate and reliable predictions shows its potential as a valuable tool for improving software maintenance processes. Future work will focus on expanding its applicability and ensuring its effectiveness across diverse project environments.  
Software, J.J. and J.K.; Writing--review & editing, G.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korea National University Development Project (2024) at Hankyong National University.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This research was supported by Hankyong National University Korea National University Development Project (2024).  
The authors declare no conflict of interest.