A Hybrid Deep Learning Model for Automatic Bug Triaging

Abstract

Maintaining software products is a continuous process where feature improvements, code refactoring, and bug fixing must be done regularly to ensure the quality of the product. Bug triaging, the process of identifying the most suitable developer to resolve a bug, is a manual process that is error-prone and resource-intensive. While automated bug triaging systems exist, these systems often struggle with inaccurate predictions and performance limitations due to unstructured and inconsistent textual data. The proposed model aims to significantly improve the predictive accuracy of automated bug triaging systems using an advanced hybrid deep learning architecture. Initially, the dataset is preprocessed and tokenized using the transformer-based BERT model, leveraging its contextual understanding of raw textual data. To further improve predictive performance, this study integrates Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks, combining CNN’s feature extraction capabilities with BiLSTM’s bidirectional contextual understanding. The model is evaluated using open-source datasets and benchmarked against multiple deep learning and machine learning models. The proposed hybrid model achieved an accuracy of 86% and an F1-score of 85%, highlighting its strong ability to predict the most suitable developer. Comparisons with other models indicate that integrating Bi-LSTM improves accuracy, outperforming the BERT-CNN, which gained 84% accuracy. Even though the XGBoost model achieved the highest accuracy of 96%, further testing revealed that it lacks contextual understanding, leading to misclassifications. Those findings indicate the proposed model’s ability to make accurate predictions, making it a more reliable solution for bug triaging.