Abstract

This study explores the optimization of automated testing frameworks for high-performance computing environments, including IoT systems. A comprehensive analysis of existing challenges is conducted, including device heterogeneity, interoperability issues, scalability constraints, and security concerns. The study identifies potential solutions through the integration of modern artificial intelligence (AI) techniques and self-healing mechanisms. The methodology is based on an analysis of publicly available scientific research. Empirical evaluations and implementation examples demonstrate improvements in testing efficiency, reliability, and adaptability, as confirmed by previous research findings. The insights presented in this study will be valuable to researchers and practitioners in high-performance computing, as well as professionals involved in the development and optimization of automated testing frameworks, aiming to ensure reliable and scalable testing of complex distributed environments. Additionally, this study may serve as a valuable resource for project managers and strategists in the IT industry interested in adopting innovative testing methods to enhance the efficiency and resilience of mission-critical applications.

References

  1. 1. Vemula S. R. exploring challenges and opportunities in test automation for iot devices and systems //International journal of computer engineering and technology (IJCET). – 2024. – Vol. 15 (4). – pp. 39-52.
  2. 2. Nama P., Reddy P., Pattanayak S. K. Artificial Intelligence for Self-Healing Automation Testing Frameworks: Real-Time Fault Prediction and Recovery //Artificial Intelligence. – 2024. – Vol. 64 (3). – pp. 111-137.
  3. 3. Pelluru K. AI-driven DevOps orchestration in cloud environments: Enhancing efficiency and automation //Integrated Journal of Science and Technology. – 2024. – Vol. 1 (6). – pp. 1-15.
  4. 4. Vaithianathan M. et al. Integrating AI and Machine Learning with UVM in Semiconductor Design //ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume. – 2024. – Vol. 2. – pp. 37-51.
  5. 5. Bachir S. et al. Towards autonomic educational cyber physical systems //2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). – IEEE. - 2019. – pp. 1198-1204.
  6. 6. Aung Y. L. et al. Generative AI for Internet of Things Security: Challenges and Opportunities //arXiv preprint arXiv:2502.08886. – 2025. – pp. 1-7.
  7. 7. Al Bashar M. et al. The impact of advanced robotics and automation on supply chain efficiency in industrial manufacturing: a comparative analysis between the us and Bangladesh //Economics, Development & Project Management. – 2024. – Vol. 3 (3). – pp. 28-41.
  8. 8. Feldt R. et al. Towards autonomous testing agents via conversational large language models //2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). – IEEE. - 2023. – pp. 1688-1693.
  9. 9. Battina D. S. Artificial intelligence in software test automation: A systematic literature review //International Journal of Emerging Technologies and Innovative Research (www. jetir. org| UGC and issn Approved), ISSN. – 2019. – pp. 2349-5162.
  10. 10. Khankhoje R. An In-Depth Review of Test Automation Frameworks: Types and Trade-offs //International Journal of Advanced Research in Science, Communication and Technology (IJARSCT). – 2023. – Vol. 3 (1). – pp. 55-64.
  11. 11. Pelluru K. AI-driven DevOps orchestration in cloud environments: Enhancing efficiency and automation //Integrated Journal of Science and Technology. – 2024. – Vol. 1 (6). – pp 1-15.
  12. 12. SchΓ€fer M. et al. An empirical evaluation of using large language models for automated unit test generation //IEEE Transactions on Software Engineering. – 2023. – Vol. 50 (1). – pp. 85-105.