Automated code refactoring using machine learning
Table Of Contents
- <p>
- 1.Introduction<br>
- 1.1Significance of automated code refactoring in software development<br>
- 1.2Research objectives<br>
- 2.Literature review<br>
- 2.1Fundamentals of code refactoring and best practices<br>
- 2.2Applications of machine learning in automated software engineering<br>
- 2.3Challenges and opportunities in automated code refactoring<br>
- 3.Data collection and feature extraction<br>
- 3.1Selection of code repositories and refactoring patterns<br>
- 3.2Extraction of code metrics and features<br>
- 3.3Ethical considerations and code ownership<br>
- 4.Automated refactoring model development<br>
- 4.1Selection of machine learning algorithms for refactoring suggestion generation<br>
- 4.2Model training and validation<br>
- 4.3Performance evaluation and impact analysis<br>
- 5.Case studies and experiments<br>
- 5.1Application of automated refactoring to real-world software projects<br>
- 5.2Comparative analysis with manual refactoring efforts<br></p>
Project Abstract
<p> Code refactoring is an essential practice in software development for improving code quality, maintainability, and performance. This project aims to explore the application of machine learning techniques for automated code refactoring, with a focus on identifying refactoring opportunities, generating refactoring suggestions, and evaluating the impact of automated refactoring on software quality. The study will involve analyzing code repositories, feature extraction, model training, and empirical evaluation using real-world software projects. The outcomes of this project will contribute to advancing automated software engineering practices and supporting developers in maintaining high-quality codebases. <br></p>Project Overview
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