Enhancing Software Fault Detection with Deep Reinforcement Learning: A Q-Learning Approach

With the increasing complexity of software systems, traditional software fault detection methods are becoming less effective. This paper proposes a novel approach that leverages Deep Reinforcement Learning (DRL) to improve software fault detection. DRL, a subset of machine learning, has shown promising results in various domains and has the potential to revolutionize software engineering practices. By formulating software fault detection as a reinforcement learning task, we develop a DRL-based model using Q-learning that can learn complex fault patterns and make accurate predictions. Our approach also incorporates feature extraction using Random Forest and Naïve Bayes. We evaluate our method using real-world software datasets, demonstrating its potential to enhance fault detection accuracy and contribute to more reliable and efficient software development processes.