Global Journal of Multidisciplinary and Applied Sciences

The Impact of AI on Software Debugging and Maintenance

Muhammad Bilal Khan

Associate Professor, Department of Software Engineering, University of Engineering & Technology (UET), Lahore, Pakistan

Keywords: Artificial Intelligence, Software Debugging, Machine Learning, Code Maintenance, Predictive Analytics

Abstract

Artificial Intelligence (AI) has revolutionized software engineering practices by automating complex tasks and improving decision-making efficiency. Among its most significant applications lies the domain of software debugging and maintenance, where AI-driven tools and techniques are reshaping the traditional approaches to error detection, code analysis, and system optimization. Machine learning algorithms, natural language processing (NLP), and automated reasoning now empower developers to identify software bugs more accurately and efficiently than conventional manual methods. This paper explores the transformative role of AI in debugging and maintenance, emphasizing its contribution to predictive fault detection, automated code repair, and software evolution. The integration of AI reduces maintenance costs, enhances system reliability, and accelerates development cycles. However, it also introduces challenges related to model interpretability, data dependency, and trust in automated decisions. The article concludes by presenting future directions for AI-driven maintenance frameworks that combine explainable AI with software analytics for improved transparency and adaptability.