Automated Bug Detection and Auto Fix Generation by using Ml Model

D Sangeetha; R Swathi, S Pavithra Sree, K Sinduja

doi:10.15662/IJRAI.2026.0903008

Authors

D Sangeetha Assistant Professor, Department of Information Technology, Jaya Engineering College, Anna University, Chennai, Tamil Nadu, India Author
R Swathi, S Pavithra Sree, K Sinduja UG Student, Department of Information Technology, Jaya Engineering College, Anna University, Chennai, Tamil Nadu, India Author

DOI:

https://doi.org/10.15662/IJRAI.2026.0903008

Keywords:

Machine Learning. Automated Bug Detection, Auto Fix Generation Software Debugging, Large Language Model (LLM), Intelligent System, Code Analysis, Error Detection, Software Development, Pattern Recognition, Real-time Feedback, Code Correction Developer Productivity, Scalable System, Al-based Debugging. Bug Classification, Self-learning System

Abstract

This project is a developing an Intelligent Machine Learning based system for automated bug detection and auto fix generation in software applications. Debugging is one of the most time-consuming and challenging tasks in software development, especially for beginners. In this system, the user provides source code or error messages as input. The system analyzes the code using Machine Leaming and Natural Language Processing (NLP) techniques trained on large datasets of buggy and corrected code. Based on learned patterns, the model identifies the type of bug and generates appropriate fix suggestions. The system works similar to a chatbot, providing interactive and user-friendly debugging assistance. This intelligent approach reduces human effort. improves coding efficiency, and helps developers learn better programming practices. The proposed system is cost-effective, scalable, and suitable for real-time software development environments. It provides real-time feedback, enabling immediate correction of mistakes during development. The machine learning model continuously updates its knowledge base as new types of bugs are encountered.

The proposed system accepts source code or error messages as input from the user. It processes this input using advanced ML models trained on large datasets containing buggy code samples and their corresponding corrected versions. By learning patterns, structures, and common error types from historical data, the model is capable of identifying syntax errors, logical bugs, runtime exceptions, and semantic inconsistencies in the code. The integration of NLP techniques enables the system to interpret error messages and understand programming context, making it more adaptable across different programming languages and development environments.

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