Authors :
R. Lavanya Bai; Dr. C. H. Saradadevi; S. Shri Preetha
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/7zh7n2d3
DOI :
https://doi.org/10.38124/ijisrt/25may1017
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Generative Artificial Intelligence (GenAI) is rapidly transforming the software testing landscape, introducing both
groundbreaking opportunities and significant challenges. Traditional testing techniques are increasingly inadequate for evaluating GenAI
systems, which generate novel, diverse, and often unpredictable outputs. This has led to fundamental issues such as the "oracle problem,"
difficulties in test adequacy assessment, and concerns about bias, privacy, and explainability. Across academic and industry perspectives,
researchers and practitioners highlight the need for new methodologies—such as metamorphic testing, differential testing, and diversity-
based adequacy measures—to address the unique complexities of GenAI. Moreover, the role of AI in supporting test generation,
prioritization, and automation is expanding, promising increased efficiency and scalability. However, ensuring trustworthiness,
accountability, and ethical deployment of GenAI in critical domains like healthcare and finance requires careful integration of human
oversight, rigorous validation techniques, and the development of interpretable models. This body of work collectively underscores the
urgent need for interdisciplinary efforts to develop robust, adaptive, and transparent testing frameworks tailored for GenAI systems.
Keywords :
Software Testing; Large Language Model(LLM); Test Case Generation; Generative Artificial Intelligence (Genai).
References :
- M. Islam, F. Khan, S. Alam, and M. Hasan, “Artificial Intelligence in Software Testing: A Systematic Review,” Proc. IEEE TENCON, 2023, doi: 10.1109/TENCON58879.2023.10322349.
- A. Aleti, “Software Testing of Generative AI Systems: Challenges and Opportunities,” arXiv preprint arXiv:2309.03554, Sep. 2023. [Online]. Available: https://arxiv.org/abs/2309.03554
- L. Layman and R. Vetter, “Generative Artificial Intelligence and the Future of Software Testing,” IEEE Computer, vol. 57, no. 1, pp. 40–48, Jan. 2024.
- M. Islam, F. Khan, S. Alam, and M. Hasan, “Artificial Intelligence for Software Testing: Perspectives and Practices,” Proc. of IEEE CCITC, 2021.
- OpenAI, “GPT-4 Technical Report,” 2023. [Online]. Available: https://openai.com/research/gpt-4
Generative Artificial Intelligence (GenAI) is rapidly transforming the software testing landscape, introducing both
groundbreaking opportunities and significant challenges. Traditional testing techniques are increasingly inadequate for evaluating GenAI
systems, which generate novel, diverse, and often unpredictable outputs. This has led to fundamental issues such as the "oracle problem,"
difficulties in test adequacy assessment, and concerns about bias, privacy, and explainability. Across academic and industry perspectives,
researchers and practitioners highlight the need for new methodologies—such as metamorphic testing, differential testing, and diversity-
based adequacy measures—to address the unique complexities of GenAI. Moreover, the role of AI in supporting test generation,
prioritization, and automation is expanding, promising increased efficiency and scalability. However, ensuring trustworthiness,
accountability, and ethical deployment of GenAI in critical domains like healthcare and finance requires careful integration of human
oversight, rigorous validation techniques, and the development of interpretable models. This body of work collectively underscores the
urgent need for interdisciplinary efforts to develop robust, adaptive, and transparent testing frameworks tailored for GenAI systems.
Keywords :
Software Testing; Large Language Model(LLM); Test Case Generation; Generative Artificial Intelligence (Genai).
