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Introduction

Automated software security testing using artificial intelligence (AI) has emerged as a critical area of research in the field of software engineering. With the increasing complexity and sophistication of software systems, ensuring their security has become a daunting task for developers and organizations. Traditional methods of software testing are often time-consuming, labor-intensive, and prone to human error, making them inadequate for identifying security vulnerabilities in modern software applications.

AI-based approaches offer a promising solution to this problem by leveraging machine learning algorithms and data analytics techniques to automate the process of identifying and mitigating security risks in software systems. By harnessing the power of AI, developers can quickly and efficiently detect, analyze, and remediate security vulnerabilities, thereby improving the overall security posture of their software applications.

This thesis explores the potential of automated software security testing using AI and its implications for the field of software engineering. Through a comprehensive literature review, research methodology, and discussion of findings, this research aims to provide valuable insights into the effectiveness and feasibility of AI-based approaches for software security testing.

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 Security Testing
2.2 Traditional Methods vs. AI-Based Approaches
2.3 Machine Learning Algorithms for Security Testing
2.4 Data Analytics Techniques for Security Testing
2.5 Automated Vulnerability Detection Tools
2.6 Case Studies on AI-Based Security Testing
2.7 Challenges and Limitations of AI in Security Testing
2.8 Best Practices for Implementing AI-Based Security Testing
2.9 Future Trends in AI-Based Security Testing
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Analysis Procedures
3.5 Ethical Considerations
3.6 Research Instruments
3.7 Validity and Reliability
3.8 Limitations of the Research Methodology

Chapter 4: Discussion of Findings
4.1 Overview of AI-Based Security Testing Tools
4.2 Comparative Analysis of AI-Based vs. Traditional Security Testing
4.3 Case Studies on Successful Implementation of AI in Security Testing
4.4 Impact of AI on Software Security Posture
4.5 Adoption Challenges and Best Practices
4.6 Recommendations for Future Research
4.7 Implications for Software Engineering Practice

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions to the Field of Software Engineering
5.3 Implications for Future Research
5.4 Conclusion and Recommendations
5.5 Reflection on the Thesis Research Process.

Thesis Overview on Automated Software Security Testing using AI:

Automated software security testing using artificial intelligence (AI) is a critical area of research that aims to enhance the security posture of modern software applications. This thesis explores the potential of AI-based approaches for identifying and mitigating security vulnerabilities in software systems. Through a comprehensive literature review, research methodology, and discussion of findings, this research provides valuable insights into the effectiveness and feasibility of AI in software security testing.

The literature review discusses the evolution of software security testing, traditional methods vs. AI-based approaches, machine learning algorithms, data analytics techniques, automated vulnerability detection tools, case studies, challenges, best practices, and future trends in AI-based security testing. The research methodology outlines the design, data collection methods, sampling techniques, analysis procedures, ethical considerations, instruments, and validity of the research.

The discussion of findings includes an overview of AI-based security testing tools, a comparative analysis of AI-based vs. traditional security testing, case studies, impact of AI on software security posture, adoption challenges, best practices, recommendations, and implications for software engineering practice. The conclusion and summary provide a summary of key findings, contributions, implications, recommendations, and reflection on the research process.

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