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Introduction

Automated software testing has become increasingly important in the software development process as it helps to improve the efficiency and effectiveness of testing activities. With the advancements in Artificial Intelligence (AI) technology, automated software testing using AI has emerged as a promising approach to enhance the quality of software testing.

This thesis explores the use of AI in automated software testing to address the challenges faced in traditional testing methods. The study aims to investigate the potential benefits of using AI in software testing and to develop an AI-based automated testing framework.

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 Software Testing
2.2 Traditional Software Testing Methods
2.3 Role of AI in Software Testing
2.4 AI Techniques for Automated Testing
2.5 Advantages of Automated Testing using AI
2.6 Challenges of Automated Testing using AI
2.7 Case Studies on AI-based Testing
2.8 Comparison of AI-based Testing Tools
2.9 Future Trends in AI-based Testing
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 AI Algorithms Selection
3.4 Testing Scenarios Generation
3.5 Test Case Execution
3.6 Performance Evaluation Metrics
3.7 Validation and Verification
3.8 Ethical Considerations

Chapter 4: System Implementation
4.1 Environment Setup
4.2 Data Collection and Preparation
4.3 Model Training and Testing
4.4 Integration with Testing Tools
4.5 Testing Scenarios Implementation
4.6 Performance Evaluation
4.7 Results Analysis
4.8 Limitations and Future Enhancements

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview on Automated Software Testing using AI

Automated software testing using AI is an emerging trend in the field of software development that offers new opportunities for improving the efficiency and effectiveness of testing activities. This thesis aims to provide insights into the use of AI in automated software testing, exploring the potential benefits, challenges, and future trends in this area.

The literature review in this thesis highlights the importance of automated testing and the role of AI in enhancing testing processes. Various AI techniques and tools used in automated testing are discussed, along with case studies and comparisons of AI-based testing tools. The review also explores future trends and challenges in AI-based testing.

The system design and methodology chapter outlines the framework for implementing AI in automated software testing, including system architecture, data preprocessing, AI algorithms selection, testing scenarios generation, and performance evaluation metrics. Ethical considerations in AI-based testing are also addressed in this chapter.

The system implementation chapter details the implementation process of the AI-based automated testing framework, including environment setup, data collection and preparation, model training and testing, integration with testing tools, and results analysis. The limitations of the system and future enhancements are also discussed.

In conclusion, this thesis provides a comprehensive overview of automated software testing using AI, highlighting the potential benefits and challenges of integrating AI into testing processes. The findings of this study contribute to the understanding of AI-based testing and provide recommendations for future research in this area.

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