AI - Powered Code Bug Detection and Fixing System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802475Keywords:
Bug Detection, Automated Debugging, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Static Analysis, Software Engineering, Code Repair, Transformer Models, Python, Neural NetworksAbstract
This research introduces an innovative framework for automated bug detection in software development, leveraging machine learning. The framework demonstrates real-time feedback capabilities and seamless integration into developers’ workflows. This work contributes to advancing automated bug detection and the framework establishes a foundation for future research, offering insights into the evolving landscape of automated bug detection. The primary focus of this framework is to provide real-time feedback to developers and seamlessly integrate it into their workflows. By leveraging machine learning, the framework aims to enhance the efficiency and accuracy of bug detection processes. The incorporation of real-time feedback is particularly significant as it enables developers to promptly address issues during the development phase, reducing the likelihood of bugs persisting into the final product. This research contributes significantly to the field of automated bug detection by not only presenting a novel framework but also demonstrating its practical applicability in real-time scenarios. The seamless integration into developers’ workflows emphasizes the practicality and usability of the framework, showcasing its potential for widespread adoption in the software development industry. Moreover, the work lays the groundwork for future research endeavors in the realm of automated bug detection, offering valuable insights into the evolving landscape of this crucial aspect of software development
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