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Introduction:

Automated code generation using Artificial Intelligence (AI) is a cutting-edge technology that holds great promise for improving the software development process. By leveraging AI algorithms, developers can generate code automatically, saving time and effort while also reducing the likelihood of errors. This thesis aims to explore the potential of AI in code generation and its impact on software development practices.

Table of Contents:

Chapter 1: Introduction
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 2: Literature Review
2.1 Overview of Automated code generation
2.2 Evolution of AI in software development
2.3 Current trends in code generation using AI
2.4 Challenges in automated code generation
2.5 Benefits of AI in code generation
2.6 AI algorithms for code generation
2.7 Case studies of AI-driven code generation tools
2.8 Comparison of AI-based code generation tools
2.9 Future prospects of AI in code generation
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 System architecture for automated code generation
3.2 Data collection and preprocessing
3.3 Selection of AI algorithms for code generation
3.4 Training and testing of the code generation model
3.5 Evaluation metrics for code quality
3.6 Integration of AI code generation tool with IDE
3.7 User interface design for code generation tool
3.8 System validation and testing
3.9 Ethical considerations in AI code generation
3.10 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Implementation of AI code generation model
4.2 Development of code generation tool
4.3 Testing and debugging of the tool
4.4 Integration with existing software development workflows
4.5 Performance optimization of code generation tool
4.6 User feedback and iteration
4.7 Documentation of code generation tool
4.8 Maintenance and support plan
4.9 Challenges faced during implementation
4.10 Summary of system implementation

Chapter 5: Conclusion and Summary
5.1 Recap of the research objectives
5.2 Summary of key findings
5.3 Contributions to the field of automated code generation
5.4 Implications for software development practices
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview:

Automated code generation using AI is a groundbreaking technology that aims to revolutionize the software development process. This thesis explores the potential of AI in code generation and its implications for developers. The literature review provides an overview of current trends in code generation using AI, challenges faced, benefits obtained, and future prospects. The system design and methodology chapter details the architecture of the code generation tool, selection of AI algorithms, data preprocessing, model training, evaluation metrics, and integration with IDE. The system implementation chapter covers the development, testing, optimization, user feedback, and maintenance of the code generation tool. The conclusion and summary chapter recaps the research objectives, key findings, contributions to the field, and recommendations for future research. Overall, this thesis sheds light on the promising opportunities offered by AI in automated code generation and its potential to transform software development practices.

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