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.

Thesis Overview

The project on "Automated Code Review System Using Machine Learning" focuses on the development of a sophisticated software system that utilizes machine learning algorithms to automate the process of reviewing and analyzing code. This research overview aims to provide a comprehensive understanding of the project, its significance, objectives, methodology, and potential impact. In the realm of software development, code review plays a crucial role in ensuring the quality, reliability, and security of the codebase. However, manual code review processes are time-consuming, error-prone, and require significant human effort. By leveraging machine learning techniques, this project aims to streamline and enhance the code review process by automating various aspects of code analysis. The primary objective of the project is to design and implement an intelligent code review system that can automatically detect code defects, vulnerabilities, and inefficiencies in a given codebase. By training machine learning models on a large dataset of code samples and their corresponding review outcomes, the system will be able to learn patterns and trends that indicate problematic code segments. The research methodology involves several key steps, including data collection, preprocessing, feature extraction, model training, and evaluation. The system will be built using state-of-the-art machine learning libraries and frameworks, such as TensorFlow or scikit-learn, to develop and deploy robust code analysis models. The automated code review system is expected to offer several advantages over manual code review processes, including improved accuracy, consistency, and efficiency. By automating routine code analysis tasks, software developers can focus their time and effort on more creative and high-level problem-solving activities. The potential impact of the project is significant, as it has the potential to revolutionize the way code review is conducted in the software development industry. By integrating machine learning capabilities into code review tools, organizations can reduce the time and resources required for code maintenance, improve code quality, and enhance overall software security. In conclusion, the "Automated Code Review System Using Machine Learning" project represents a cutting-edge research endeavor that aims to harness the power of machine learning to enhance code review processes. By automating code analysis tasks, the system has the potential to improve software quality, reduce development costs, and accelerate the software development lifecycle.