Paper Contents
Abstract
Debugging code is a time-intensive task in software development. This project presents an AI-powered Self-Healing Code Debugger that automatically detects, analyses ,and fixes code errors using machine learning and natural language processing. By combining static and runtime analysis with models like GPT, the system suggests intelligent code fixes and validates them through automated testing. A feedback loop enables continuous learning, enhancing the systems accuracy over time. Designed with modular architecture, the debugger integrates easily with IDEs, CICD pipelines, and version control systems. It significantly reduces manual effort, speeds up the development process, and promotes more reliable, self-sustaining software systems.
Copyright
Copyright © 2025 TSKS JYOTHIRMAYI. This is an open access article distributed under the Creative Commons Attribution License.
