Machine Learning Techniques for Predictive Bug Detection and Software Error Prevention in IT Systems

Abstract

In modern software development, ensuring high-quality and error-free systems is a critical challenge. Traditional debugging and testing approaches are often reactive, detecting defects only after they occur, which increases development costs and delays deployment. Machine Learning (ML) techniques offer a proactive solution by analyzing historical software data, identifying patterns, and predicting potential bugs before they manifest. This study explores various ML approaches, including supervised learning, unsupervised learning, and deep learning, for predictive bug detection and software error prevention. By leveraging software metrics, version control histories, and code change data, predictive models can assess the likelihood of defects in different modules. The research highlights the integration of these models into development pipelines to prioritize testing efforts, reduce defect density, and enhance system reliability. Results indicate that ML-driven predictive models significantly improve software quality, optimize resource allocation, and contribute to more robust, error-resilient IT systems.