# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 6 Related Work  
Several studies have addressed the challenge of predicting bug report priorities using a variety of techniques, including machine learning (ML), deep learning (DL), and hybrid approaches. The following section highlights key contributions from previous research:  
[PERSON] et al. [19] proposed a CNN-based model for priority prediction. They applied natural language processing (NLP) techniques to preprocess bug report text and extract features before predicting priorities using a convolutional neural network (CNN). Similarly, [PERSON] et al. [20] utilized a text-emotion-analysis-based priority prediction method, combining NLP and ML algorithms to achieve accurate predictions. [PERSON] et al. [21] developed a priority prediction technique incorporating textual features, time, similar reports, authors, severity, and product attributes from Eclipse datasets. [PERSON] et al. [22] introduced a bug-priority prediction system based on artificial neural networks (ANN) across five products, showing an improved Precision, Recall, and F-measure through three cross-validation tests. [PERSON] et al. [23] employed an SVM-based classification approach to predict bug priorities for Eclipse, while [PERSON] et al. [15] used SVM, Naive Bayes (NB), and k-nearest neighbors (KNN) for the same task. Their results showed that SVM and KNN achieved over 70% Accuracy, outperforming NB. [PERSON] et al. [16] compared NB, decision trees, and random forest algorithms, finding that random forest and decision trees performed better than NB for bug priority prediction. [PERSON] et al. [24] proposed a regression-based approach to predict bug report priorities, assigning ordinal values to reduce the discrepancy between priority levels. [PERSON] et al. [25] evaluated a five-layer RNN-LSTM model against SVM and KNN on the JIRA dataset, achieving a superior Accuracy (90%) and a 15.2% improvement in the F-measure over KNN. [PERSON] et al. [26] applied entropy-based metrics to NB and DL for predicting bug priorities in OpenOffice, showing that entropy-enhanced DL models outperformed their NB counterparts. [PERSON] et al. [27] used NB, simple logistic regression, and Random Trees for classification, with logistic regression yielding the best results. [PERSON] et al. [28] addressed class imbalances using SMOTE and developed a framework, CaPBUG, which achieved a 90.43% accuracy using NB, Random Forest, Decision Tree, and logistic regression. [PERSON] et al. [29] utilized heterogeneous text graphs and graph convolutional networks (GCNs) to extract word-level meanings from bug reports across multiple datasets, achieving strong results with a weighted loss function. [PERSON] et al. [30] tested multiple ML algorithms, including NB, Decision Trees, logistic regression, Random Forest, and AdaBoost, on open-source projects such as Hadoop and Spark. They showed consistent performance with polynomial NB and cross-validation techniques. [PERSON] et al. [31] used over 82,000 bug reports to train deep neural networks after feature extraction and preprocessing with NLP. [PERSON] et al. [32] combined CNNs with emotion analysis, improving F1 scores by over 24% through vectorized text representations and domain-specific emotional features. [PERSON] et al. [33] incorporated developer-centered social and technological features to predict bug priorities, achieving significant improvements in AUC-ROC and MCC scores. [PERSON] et al. [34] improved Precision and Recall using SVM and NB models enhanced with Pearson correlation-based feature selection and information gain. [PERSON] et al. [35] presented four class-level odor prioritization approaches, showing that their method was, on average, 20% more accurate than the baseline. [PERSON] et al. [36; 37; 38] proposed a series of models addressing diverse challenges in IoT sensor networks. First, they introduced a heterogeneous data access model designed to manage vehicle data in diverse IoT sensor monitoring networks, aiming to enhance data access efficiency and enable real-time processing of streaming vehicle data.