Generative AI in Software Testing: Current Trends and Future Directions
Tanish Singla, Qusay H. Mahmoud

TL;DR
This paper reviews how Generative AI can transform software testing by improving test coverage, efficiency, and cost-effectiveness, highlighting current applications, challenges, and future opportunities.
Contribution
It provides a comprehensive overview of Generative AI's role in software testing, including methods to enhance its efficiency and practical insights for future research and application.
Findings
Generative AI improves test case generation and validation.
AI can streamline testing processes, increasing robustness.
Potential for significant cost and time reductions in testing.
Abstract
This paper investigates current software testing systems and explores how artificial intelligence, specifically Generative AI, can be integrated to enhance these systems. It begins by examining different types of AI systems and focuses on the potential of Generative AI to transform software testing processes by improving test coverage, increasing efficiency, and reducing costs. The study provides a com-prehensive overview of the current applications of AI in software testing, emphasizing its significant contributions in areas such as test case generation and validation. Through an extensive literature re-view, it highlights how Generative AI can streamline these processes, resulting in more robust and thorough testing outcomes. The paper also examines methods to improve the efficiency of Generative AI systems, such as prompt engineering and fine-tuning. Additionally, it explores the use…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Software System Performance and Reliability · Software Engineering Techniques and Practices
