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Introduction

Software maintenance and evolution are crucial aspects of software development that ensure the longevity and usability of software systems. With the growing complexity of software systems, traditional manual maintenance and evolution practices are no longer sufficient to keep up with the rapid changes and updates required in today’s dynamic software landscape. Artificial Intelligence (AI) technologies have emerged as promising solutions to automate and optimize software maintenance and evolution processes.

This thesis aims to explore the potential of AI-driven software maintenance and evolution and its implications for the future of software development. By leveraging AI techniques such as machine learning, natural language processing, and knowledge representation, software engineers can enhance their capabilities in managing and evolving complex software systems more effectively and efficiently.

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 Evolution of Software Maintenance
2.2 AI Technologies in Software Development
2.3 AI in Software Maintenance and Evolution
2.4 Benefits of AI-driven Maintenance
2.5 Challenges and Limitations
2.6 Current Trends in AI-driven Software Development
2.7 Case Studies and Success Stories
2.8 Future Directions in AI-driven Maintenance
2.9 Ethical Considerations
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 Research Framework
3.2 Data Collection and Analysis
3.3 AI Techniques and Tools
3.4 Model Development
3.5 Evaluation Metrics
3.6 Validation and Testing
3.7 Implementation Strategy
3.8 Ethical Considerations

Chapter 4: System Implementation
4.1 System Architecture
4.2 Data Preprocessing
4.3 Feature Engineering
4.4 Model Training
4.5 Hyperparameter Tuning
4.6 Deployment and Integration
4.7 Performance Evaluation
4.8 Results and Analysis

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations and Future Research Directions
5.5 Conclusion

Thesis Overview:

Artificial intelligence (AI) technologies have revolutionized various industries, including software development. The integration of AI in software maintenance and evolution has the potential to significantly improve the efficiency and effectiveness of software development processes. This thesis explores the application of AI-driven techniques in software maintenance and evolution, aiming to address the challenges faced by software engineers in managing and evolving complex software systems.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, thesis structure, and definitions of terms. Chapter 2 presents a comprehensive review of the literature on AI technologies in software development, highlighting the evolution of software maintenance, benefits, challenges, current trends, case studies, ethical considerations, and future directions.

In Chapter 3, the research framework, data collection, analysis, AI techniques, model development, evaluation metrics, validation, testing, and implementation strategy are discussed. Chapter 4 details the system architecture, data preprocessing, feature engineering, model training, hyperparameter tuning, deployment, integration, performance evaluation, and results.

Lastly, Chapter 5 concludes the thesis, summarizing the findings, contributions, practical implications, limitations, and future research directions. Overall, this thesis aims to provide valuable insights into the potential of AI-driven software maintenance and evolution in shaping the future of software development.

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