Optimization of Automated Testing Frameworks for High-Performance Environments

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.