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Introduction

Automated software performance optimization using Artificial Intelligence (AI) has become an increasingly important topic in the field of computer science and software engineering. With the growing complexity of software applications and the increasing demand for high performance and reliability, the traditional manual methods of software performance optimization are no longer sufficient to meet the requirements of modern software systems. AI techniques such as machine learning and deep learning have shown great potential in automatically optimizing software performance by learning from data and making intelligent decisions.

This thesis aims to explore the application of AI in automated software performance optimization and investigate its effectiveness in improving the performance of software applications. The research will focus on developing AI-based algorithms and tools that can automatically identify performance bottlenecks in software applications, analyze the root causes of these bottlenecks, and recommend optimization strategies to improve the performance of the software system.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objectives of study
1.5 Limitations 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 software performance optimization
2.2 Traditional methods of software performance optimization
2.3 AI techniques for software performance optimization
2.4 Case studies on AI-based software performance optimization
2.5 Challenges and limitations of AI-based software performance optimization
2.6 Recent advancements in AI-based software performance optimization
2.7 Comparison of AI-based and traditional methods
2.8 Future research directions in AI-based software performance optimization
2.9 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 AI algorithms for performance optimization
3.5 Evaluation metrics
3.6 Experiment setup
3.7 Validation and testing
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Performance analysis of AI-based optimization
4.2 Comparison with traditional methods
4.3 Impact of AI-based optimization on software performance
4.4 Interpretation of results
4.5 Practical implications
4.6 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for practice
5.4 Limitations of the study
5.5 Areas for future research
5.6 Conclusion

Thesis Overview

Automated software performance optimization using AI is a cutting-edge research topic that aims to improve the performance and efficiency of software applications through the application of artificial intelligence techniques. This thesis focuses on exploring the potential of AI in automatically optimizing software performance by developing algorithms and tools that can identify performance bottlenecks, analyze their root causes, and recommend optimization strategies.

The literature review in Chapter 2 provides an overview of traditional methods of software performance optimization, AI techniques for performance optimization, case studies on AI-based optimization, challenges and limitations, recent advancements, and future research directions. The research methodology in Chapter 3 outlines the research design, data collection, preprocessing, AI algorithms, evaluation metrics, experiment setup, validation, and ethical considerations.

Chapter 4 discusses the findings of the research, including performance analysis of AI-based optimization, comparison with traditional methods, impact on software performance, interpretation of results, practical implications, and recommendations for future research. Finally, Chapter 5 summarizes the key findings, contributions to the field, implications for practice, limitations of the study, areas for future research, and concludes the thesis on Automated software performance optimization using AI.

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