Thesis Abstract

Abstract
Automated Code Review System using Machine Learning is a cutting-edge project that aims to revolutionize the software development process by leveraging machine learning techniques to enhance code quality and efficiency. In this thesis, we propose the development of an automated code review system that will assist software developers in identifying and addressing code quality issues in a timely and accurate manner. The introduction provides an overview of the project, highlighting the importance of code review in software development and the challenges faced by developers in conducting manual code reviews. The background of the study explores the existing literature on code review processes and the role of machine learning in software development. The problem statement identifies the limitations and inefficiencies of manual code review processes, emphasizing the need for an automated system to improve code quality and developer productivity. The objectives of the study outline the specific goals and outcomes that the automated code review system aims to achieve, including the identification of code quality issues, the generation of actionable feedback, and the improvement of overall codebase maintainability. The limitations of the study acknowledge the potential challenges and constraints that may impact the development and implementation of the automated code review system. The scope of the study defines the boundaries and focus areas of the project, including the specific programming languages and code quality metrics that will be considered. The significance of the study emphasizes the potential impact of the automated code review system on software development practices, highlighting the benefits of improved code quality, faster review cycles, and enhanced collaboration among developers. The structure of the thesis provides an overview of the chapters and sections that will be included in the document, guiding the reader through the research methodology, findings discussion, and conclusion. In the literature review, we analyze existing research and tools related to automated code review and machine learning in software development. We explore the various approaches and techniques used in code analysis, defect detection, and code quality assessment, highlighting the strengths and limitations of different methodologies. The research methodology outlines the process and techniques that will be used to develop and evaluate the automated code review system. We describe the data collection methods, machine learning models, evaluation metrics, and testing procedures that will be employed to measure the performance and effectiveness of the system. In the discussion of findings, we present the results of the evaluation and analysis of the automated code review system, highlighting its strengths, weaknesses, and potential areas for improvement. We discuss the implications of our findings on software development practices and the future directions for research and development in this area. In the conclusion and summary, we summarize the key findings and contributions of the thesis, highlighting the significance of the automated code review system in improving code quality and developer productivity. We discuss the implications of our research for software development practices and outline recommendations for future work in this field.

Thesis Overview

The project titled "Automated Code Review System using Machine Learning" aims to develop an innovative system that leverages machine learning techniques to automate the process of code review in software development. This research overview provides a comprehensive explanation of the key components, objectives, and significance of the project. Software development projects often involve multiple developers working on different parts of the codebase simultaneously. As the complexity of software systems increases, ensuring code quality becomes a critical aspect of the development process. Manual code review, where developers inspect code changes line by line, is a time-consuming and error-prone task. Automated code review systems have emerged as a solution to streamline this process and improve the overall quality of software products. The proposed system will utilize machine learning algorithms to analyze code changes, identify potential issues, and provide actionable feedback to developers. By training the system on a large dataset of code repositories and historical code reviews, it will learn to recognize patterns and common errors in code submissions. This automated approach will not only speed up the code review process but also help in maintaining consistency and adherence to coding standards across the development team. The research objectives of this project include: 1. Designing and implementing a machine learning model for code analysis and review. 2. Developing a user-friendly interface for developers to interact with the automated code review system. 3. Evaluating the accuracy and effectiveness of the system in detecting common coding errors and vulnerabilities. 4. Comparing the performance of the automated system with traditional manual code review methods. The significance of this research lies in its potential to revolutionize the way code reviews are conducted in software development. By reducing the manual effort required for code inspection, developers can focus more on creative problem-solving and innovation. The automated system will also help in standardizing code quality practices across projects and organizations, leading to more robust and reliable software products. In conclusion, the "Automated Code Review System using Machine Learning" project represents a cutting-edge approach to improving code quality and developer productivity in software development. By harnessing the power of machine learning, this system has the potential to transform the way code reviews are performed, ultimately benefiting both developers and end-users alike.