AI-POWERED TEST AUTOMATION: REVOLUTIONIZING CLOUD TESTING WITH LLMS
Keywords:
AI-Driven Test Automation, Cloud-Native Testing, Serverless Infrastructure, LLM Testing Frameworks, DevOps Performance MetricsAbstract
This article presents a novel testing framework that integrates AI and Large Language Models (LLMs) with serverless architectures for cloud-native application testing. The framework demonstrates significant improvements in testing efficiency, with a 78% reduction in manual test writing time and 91% accuracy in edge case identification. Leveraging advanced prompt engineering and serverless execution, the system achieves 65% faster test execution than traditional infrastructure while reducing operational costs by 45-60%. Empirical results from over 1,400 professionals show that organizations implementing this approach are 2.4 times more likely to be elite performers in software delivery, with automated test coverage increasing by 42% year-over-year. The framework's architecture combines LLM integration, prompt engineering, serverless orchestration, and feedback analysis systems, achieving 99.82% service reliability and processing capabilities of 32,000 requests per minute.
References
Cloud Native Computing Foundation, "CNCF Annual Survey 2023: Cloud Native Adoption Trends and Challenges," CNCF, Dec. 2023. Available: https://www.cncf.io/reports/cncf-annual-survey-2023/
Sanjeev Sharma, Pradeep Singh Rawat, "Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach," Sustainable Operations and Computers, Volume 5, 2024, Pages 141-155. Available: https://www.sciencedirect.com/science/article/pii/S2666412724000114
Datadog, "The State of Serverless," Datadog Inc., 2023. Available: https://www.datadoghq.com/state-of-serverless/
Gartner, "Market Guide for Cloud Testing Tools and Quality Platforms," Gartner Research, July. 2024. Available: https://www.gartner.com/doc/reprints?id=1-2I4UJ9Y3&ct=240719&st=sb
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, Qing Wang, "Software Testing with Large Language Models: Survey, Landscape, and Vision" arXiv:2307.07221 [cs.SE], Mar. 2024. Available: https://arxiv.org/pdf/2307.07221
Mohammad Baqar, Rajat Khanda, "The Future of Software Testing: AI-Powered Test Case Generation and Validation," Software Engineering (cs.SE); Artificial Intelligence (cs.AI), Sep. 2024. Available: https://arxiv.org/abs/2409.05808
Jinfeng Wen, Zhenpeng Chen, Federica Sarro, Xuanzhe Liu, "SuperFlow: Performance Testing for Serverless Computing," arXiv:2306.01620v1 [cs.SE], Jun 2023. Available: https://arxiv.org/pdf/2306.01620
Denis Peganov, "Cloud-Native Testing: Ensuring Quality in Serverless Architectures," Medium, June. 2024. Available:
Google Cloud, "2024 State of DevOps Report," Google Cloud DORA Research Program, Jan. 2024. Available: https://services.google.com/fh/files/misc/2024_final_dora_report.pdf
Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, Raoul Joshi, "Notes from the AI Frontier: Modeling the Impact of
AI on the World Economy," McKinsey Global Institute, Sep. 2023. Available: https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/notes%20from%20the%20frontier%20modeling%20the%20impact%20of%20ai%20on%20the%20world%20economy/mgi-notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy-september-2018.ashx
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Ed Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving, "Scaling Language Models: Methods, Analysis & Insights from Training Gopher," Computation and Language (cs.CL); Artificial Intelligence (cs.AI), Jan. 2022. Available: https://arxiv.org/abs/2112.11446
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, Haoyu Wang, "Large Language Models for Software Engineering: A Systematic Literature Review," in ACM Transactions on Software Engineering and Methodology, Aug. 2024. Available: https://dl.acm.org/doi/10.1145/3695988
Virtusa Corporation, "Software Testing Trends and Predictions for 2025," Virtusa Digital Engineering Outlook, Jan. 2024. Available: https://www.virtusa.com/insights/articles/software-testing-trends-in-2025
Oyekunle Claudius Oyeniran, Oluwole Temidayo Modupe, Aanuoluwapo Ayodeji Otitoola, Oluwatosin Oluwatimileyin Abiona, Adebunmi Okechukwu Adewusi and Oluwatayo Jacob Oladapo, "A comprehensive review of leveraging cloud-native technologies for scalability and resilience in software development," International Journal of Science and Research Archive, vol. 12, no. 2, pp. 156-172, Feb. 2024. Available: https://ijsra.net/sites/default/files/IJSRA-2024-0432.pdf