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Introduction

In recent years, the use of Artificial Intelligence (AI) in software development has gained significant attention. AI-driven code refactoring tools have emerged as a promising solution to automate the process of improving the quality and maintainability of code. These tools utilize machine learning algorithms to analyze code patterns, detect code smells, and suggest appropriate refactoring techniques to developers. This thesis explores the use of AI-driven code refactoring tools and their potential impact on software development practices.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 1: Introduction
– Introduction
– Background of study
– Problem statement
– Objective of study
– Limitation of study
– Scope of study
– Significance of study
– Structure of the Thesis
– Definition of terms

Chapter 2: Literature Review
– Introduction to AI-driven code refactoring tools
– Evolution of code refactoring techniques
– Benefits and challenges of code refactoring
– Existing AI-driven refactoring tools
– Case studies on the use of AI in code refactoring
– Comparison of traditional and AI-driven code refactoring
– Future research directions in AI-driven code refactoring
– Summary of literature review

Chapter 3: System Design and Methodology
– System architecture design
– Data collection and preprocessing techniques
– Machine learning algorithms for code analysis
– Integration of AI-driven refactoring tools with IDEs
– Evaluation metrics for code refactoring
– User interface design for AI-driven refactoring tools
– Testing and validation of the system
– Summary of system design and methodology

Chapter 4: System Implementation
– Implementation of AI-driven code refactoring tool
– Integration with popular IDEs
– Testing and debugging of the system
– Performance evaluation of the tool
– User feedback and improvements
– Case studies of code refactoring using AI-driven tools
– Summary of system implementation

Chapter 5: Conclusion and Summary
– Summary of key findings
– Contributions to the field of AI-driven code refactoring
– Implications for software development practices
– Limitations and future work
– Conclusion

In this thesis, we will discuss the current state of AI-driven code refactoring tools, review existing literature in the field, present the design and methodology of our proposed system, detail the implementation of the system, and conclude with a summary of the project. The ultimate goal of this research is to showcase the potential of AI-driven code refactoring tools in improving software quality and developer productivity.

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