Automated Code Review System Using Machine Learning

Thesis Abstract

Abstract
Automated code review systems have emerged as essential tools in software development, enabling developers to identify and correct code quality issues efficiently. In this thesis, we propose the development of an Automated Code Review System using Machine Learning techniques to enhance the code review process. The system aims to automate the detection of code quality issues, improve code maintainability, and support developers in producing high-quality software. The study begins with an introduction to the importance of code review in software development, highlighting the challenges faced by developers in manual code review processes. The background of the study provides an overview of existing code review tools and techniques, emphasizing the need for automated systems to streamline the code review process. The problem statement identifies the limitations of current code review practices and the potential benefits of implementing an Automated Code Review System using Machine Learning. The objectives of the study are to develop a machine learning model capable of identifying common code quality issues, enhance the accuracy and efficiency of code review processes, and support developers in improving code quality. The study acknowledges the limitations of the research, such as the availability of labeled training data and the complexity of software projects. The scope of the study focuses on implementing the Automated Code Review System in a controlled environment and evaluating its effectiveness in detecting and addressing code quality issues. The significance of the study lies in its potential to revolutionize the code review process, saving time and effort for developers while improving the overall quality of software projects. The structure of the thesis outlines the chapters to follow, including a literature review, research methodology, discussion of findings, and conclusion. The literature review explores existing research on code review systems, machine learning techniques in software engineering, and best practices for code quality improvement. The research methodology details the process of developing and evaluating the Automated Code Review System, including data collection, model training, and evaluation metrics. The discussion of findings presents the results of implementing the Automated Code Review System, highlighting its effectiveness in detecting code quality issues and supporting developers in code improvement. The conclusion summarizes the key findings of the study, discusses implications for future research, and offers recommendations for the practical implementation of the system. In conclusion, the Automated Code Review System using Machine Learning offers a promising solution to enhance code quality and streamline the code review process in software development. By leveraging machine learning algorithms, developers can benefit from automated detection of code quality issues, leading to improved software reliability and maintainability.