Development Of A Tool That Uses Machine Learning To Review And Provide Feedback On Code Quality:- Uwamacha Chidinma H

ABSTRACT evaluating code quality, such incorporated into the tool's design. Utilizing datasetsfrom open-source projects, a machine learning model is trained to analyze code and detect common issues, such as code smells and anti-patterns. The effectiveness ofthe tool is then evaluatedagainst conventionalreview methods,focusing on metricssuch as accuracy and user satisfaction. Results indicate that the ML-based tool significantly enhances code review processes, reducing time and improving consistency. This study underscores the potential of integrating machine learning into software engineering practices, offering a scalable solution for maintaining high code quality standards. Future work will focus language support, andfurther integrating the tool into existing development workflows. vi objective feedback. The study begins with highlighting their limitations, particularly in terms of consistency and scalability. Key metrics for The increasing complexity ofsoftware development necessitates robust methods for ensuring code lead to significant issues such as bugs, security vulnerabilities, and on refining the model, expanding as complexity, readability, and maintainability, are identified and quality, as poor-quality code can high maintenance costs. This project aims to develop a tool that leverages machine learning (ML) techniques to automate the assessment of code quality, thereby providing developers with timely and an exploration of traditional code review processes,