Automated code refactoring using machine learning
Table Of Contents
<p>1. Introduction<br> 1.1 Significance of automated code refactoring in software development<br> 1.2 Research objectives<br>2. Literature review<br> 2.1 Fundamentals of code refactoring and best practices<br> 2.2 Applications of machine learning in automated software engineering<br> 2.3 Challenges and opportunities in automated code refactoring<br>3. Data collection and feature extraction<br> 3.1 Selection of code repositories and refactoring patterns<br> 3.2 Extraction of code metrics and features<br> 3.3 Ethical considerations and code ownership<br>4. Automated refactoring model development<br> 4.1 Selection of machine learning algorithms for refactoring suggestion generation<br> 4.2 Model training and validation<br> 4.3 Performance evaluation and impact analysis<br>5. Case studies and experiments<br> 5.1 Application of automated refactoring to real-world software projects<br> 5.2 Comparative analysis with manual refactoring efforts<br></p>
Thesis 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>Thesis Overview
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