Toward Reduction in False Positives Just-In-Time Software Defect Prediction Using Deep Reinforcement Learning
Document Type
Article
Publication Date
1-1-2024
Abstract
Deep Q-Network (DQN) is a popular deep reinforcement learning algorithm that has demonstrated promising results across a variety of domains. DQN presents a promising solution to the challenge of lowering false positives in software defect prediction, thereby enhancing the reliability of the prediction performance. In software defect prediction, false positives occur when the prediction model incorrectly predicts code changes to be defective. Consequently, developers waste time and resources on non-existent defects. This paper advocates for employing DQN in software defect prediction, focusing on minimizing false positives and maximizing the prediction performance. Throughout the training phase, the model learns to predict defect-prone following a reward policy aimed at reducing false results. Experimental findings show that the proposed DQN outperforms baseline classifier, improving the prediction accuracy of true defects by up to 27% when using only 20% efforts. The results show that the effectiveness of DQN in tackling false positives, thereby emphasizing the significance of incorporating dynamic reward in predicting software defects.
Keywords
Software quality, Predictive models, Codes, Inspection, Costs, Adaptation models, Training, Reinforcement learning, Deep reinforcement learning, Software defect prediction, Deep Q-Network, false positives
Divisions
Software
Funders
Universiti Malaya for the Fundamental Research Grant Scheme
Publication Title
IEEE Access
Volume
12
Publisher
Institute of Electrical and Electronics Engineers
Publisher Location
445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA