Automated Bug Detection and Correction in Software Development using Machine Learning

Abstract

Software quality assurance is a critical aspect of modern software development, where timely detection and correction of bugs can significantly enhance reliability, security, and efficiency. Traditional debugging methods are often labor-intensive, time-consuming, and prone to human error. In recent years, machine learning (ML) techniques have emerged as a promising solution for automated bug detection and correction. This paper explores the application of ML models, including supervised learning, unsupervised learning, deep learning, and transformer-based models, in identifying and fixing software defects. We discuss key methodologies such as static and dynamic code analysis, anomaly detection, and neural-based code completion for automated repair. Furthermore, we examine recent advancements, including large language models (LLMs) such as CodeBERT, Graph Neural Networks (GNNs), and reinforcement learning-based approaches that have demonstrated high accuracy in identifying and resolving software defects. We also highlight the challenges associated with data quality, generalization, and interpretability in ML-driven debugging systems. Finally, we present potential future directions in integrating AI-driven bug detection into DevOps pipelines, ensuring continuous and automated software improvement.