# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 1 Introduction  
As the adoption and complexity of software systems increase, the frequency of bugs and feature improvement requests grows correspondingly. For instance, the Eclipse and Mozilla projects reportedly handle approximately 300 bug reports daily [1]. Managing these reports efficiently is critical for maintaining project schedules and software quality. In typical open-source software development workflows [2, 3], users submit bug reports detailing issues and often provide an initial severity rating. Automating the prioritization of these reports offers significant advantages in industrial applications. Automatedsystems reduce the resource demands associated with manual review and eliminate inconsistencies arising from subjective judgment. By ensuring a more objective and consistent prioritization process, these systems enable project managers to allocate resources efficiently, accelerate response times, and enhance overall project reliability. Additionally, automation can effectively scale up systems to handle large volumes of bug reports, making it particularly valuable for managing complex and rapidly evolving software projects. Several studies have explored automated approaches for bug priority prediction. [PERSON] et al. [4] proposed a model that utilizes convolutional neural networks (CNNs) and graph neural networks (GNNs) to analyze the characteristics and interrelationships of source code files, investigating the impact of various sampling methods on bug prioritization. [PERSON] et al. [5] explored the use of traditional machine learning algorithms, enhancing performance through oversampling and feature selection techniques. Similarly, [PERSON] et al. [6] presented a bug prioritization method that employed a combination of algorithms to achieve improved results. While these approaches represent progress, the performance of bug priority prediction remains limited, necessitating further advancements in both model design and feature extraction methods. To address these limitations, this study introduces a novel bug priority prediction framework that integrates a Long Short-Term Memory-Attention (LSTM-Attention) mechanism [7] with a priority-based feature selection algorithm [8]. The methodology involves the following steps:  
* Feature Extraction: Relevant features, including product and component details, are extracted from bug reports in the repository. Additional priority-specific attributes are identified to enhance predictive accuracy. * Preprocessing and Model Training: The extracted features are preprocessed and input into the LSTM-Attention model, which combines temporal data analysis capabilities with an Attention mechanism to focus on the most relevant features for priority prediction. * Priority Prediction: The trained model predicts the priority of each bug report, providing developers and managers with automated recommendations. The proposed method was evaluated using datasets from two prominent open-source projects: Eclipse and Mozilla. Its performance was benchmarked against several baseline models, including Naive Bayes (NB), Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM. Standard evaluation metrics such as Precision, Recall, F-measure, and Accuracy were used for comparison. The results show that the proposed model achieved a prediction accuracy of 93.00% for Eclipse and 84.11% for Mozilla, representing improvements of approximately 31.11% and 40.39% over the baseline models, respectively. Statistical verification [9; 10] confirmed the significance of these improvements. The contributions of this study are as follows:  
* Innovative Algorithm Design: A priority-sensitive LSTM-Attention mechanism was developed and combined with priority-based feature extraction, resulting in enhanced bug priority prediction accuracy. * Robust Comparative Analysis: Extensive evaluations against diverse baseline models using datasets from Eclipse and Mozilla highlighted the superior performance of the proposed approach, supported by statistical significance.