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Introduction

Software performance testing is essential for ensuring that a software application can handle a high level of workload without experiencing any performance degradation. Traditionally, performance testing is carried out manually by testers, which can be time-consuming and error-prone. Automated software performance testing using Artificial Intelligence (AI) techniques has emerged as a promising solution to overcome these challenges.

This thesis aims to explore the use of AI in automated software performance testing and its potential benefits for improving the efficiency and effectiveness of performance testing. The focus will be on developing AI algorithms and techniques to automate the process of performance testing, analyze performance data, and provide insights for improving software performance.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of the 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 software performance testing
2.2 Traditional methods of performance testing
2.3 Role of AI in software testing
2.4 Automated software testing techniques
2.5 AI techniques for performance testing
2.6 Challenges in automated software performance testing
2.7 Benefits of AI in performance testing
2.8 Case studies on AI-driven performance testing
2.9 Current trends in AI-driven performance testing
2.10 Gaps in existing literature

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 AI algorithms selection
3.4 Performance metrics analysis
3.5 Experiment design
3.6 Data analysis techniques
3.7 Evaluation criteria
3.8 Validation methods

Chapter 4: Discussion of Findings
4.1 Performance testing automation using AI
4.2 AI-driven performance analysis
4.3 Performance optimization using AI insights
4.4 Comparative analysis with traditional methods
4.5 Case studies on AI-driven performance testing
4.6 Limitations of the study
4.7 Future research directions
4.8 Recommendations for implementation

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

Thesis Overview on Automated Software Performance Testing using AI

Automated software performance testing using AI has gained significant attention in recent years due to its potential to revolutionize the way performance testing is conducted. This thesis aims to explore the application of AI techniques in automating the process of software performance testing and analyze its effectiveness in improving software performance.

The literature review will provide an overview of software performance testing, traditional methods of performance testing, the role of AI in software testing, automated software testing techniques, AI techniques for performance testing, challenges, and benefits of using AI in performance testing, case studies, current trends, and gaps in the existing literature.

The research methodology will outline the research design, data collection methods, AI algorithms selection, performance metrics analysis, experiment design, data analysis techniques, evaluation criteria, and validation methods used in the study.

The discussion of findings will focus on performance testing automation using AI, AI-driven performance analysis, performance optimization using AI insights, comparative analysis with traditional methods, case studies, limitations of the study, future research directions, and recommendations for implementation.

In conclusion, this thesis will provide a comprehensive analysis of the application of AI in automated software performance testing and its potential to revolutionize the field of software testing. It will also highlight the contributions to the field, implications for practice, limitations of the study, recommendations for future research, and the overall conclusion of the research.

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