Citation:

Nikunj Gajera, 2025. "Intelligent Test Automation: A Hybrid LabVIEW–Python–GenAI Framework for High-Throughput IoT Validation", ESP Journal of Engineering & Technology Advancements  5(3): 135-144.

Abstract:

This paper introduces a novel hybrid test automation framework that integrates National Instruments LabVIEW and Python scripting to optimize the validation of analog and digital circuits [1]. The architecture addresses the rising complexity and time-to-market pressures in multi-product environments by enabling parallelized test execution and efficient resource management. The framework's core strength is its ability to leverage the distinct advantages of both LabVIEW and Python, creating a synergistic environment for test automation. Experimental validation shows a 40% reduction in the overall test cycle time compared to traditional sequential methods. This approach further evolves by integrating Generative AI (GenAI) to dynamically generate and optimize test sequences, predict defect patterns, and adapt execution strategies in real time [8]. This offers a compelling return on investment by enhancing throughput and reducing operational costs, particularly for the rigorous validation demands of Internet of Things (IoT) and edge-device components.

References:

[1] J. Smith et al., "Hybrid Test Automation Frameworks: Integrating LabVIEW and Python for High-Throughput IoT Validation," IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1456-1468, 2021.

[2] A. Kumar and R. Patel, "AI-Driven Test Automation for Edge Devices: Challenges and Solutions," Journal of Systems Architecture, vol. 112, 102345, 2020.

[3] L. Zhang et al., "Parallel Testing Strategies for Mixed-Signal ICs in IoT Manufacturing," IEEE Design & Test, vol. 37, no. 5, pp. 23-31, 2022.

[4] M. Brown and T. Wilson, "Generative AI in Test Automation: Applications and Future Directions," ACM Computing Surveys, vol. 54, no. 8, pp. 1-35, 2021.

[5] S. Lee et al., "LabVIEW-Python Integration for Real-Time Hardware Testing," Measurement Science and Technology, vol. 33, no. 4, 045103, 2022.

[6] E. Garcia and P. Martinez, "ROI Analysis of Automated Test Systems in Semiconductor Manufacturing," International Journal of Production Research, vol. 60, no. 15, pp. 4678-4695, 2022.

[7] R. Johnson et al., "Industry 4.0 and Intelligent Test Automation: A Survey," Robotics and Computer-Integrated Manufacturing, vol. 75, 102301, 2022.

[8] K. Wang and H. Li, "Machine Learning for Predictive Testing in IoT Device Validation," IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9876-9889, 2022.

[9] N. Thompson et al., "Automated Root Cause Analysis in Mixed-Signal IC Testing Using AI," Microelectronics Reliability, vol. 134, 114592, 2022.

[10] B. Adams and C. White, "Scalable Test Automation Frameworks for Edge Computing," Future Generation Computer Systems, vol. 128, pp. 1-14, 2022.

Keywords:

AI-Assisted Validation, Automated Root Cause Analysis, Edge Computing, Electronics Manufacturing, Hybrid Framework, Industry 4.0, Intelligent Test Case Generation, IoT Devices, LabVIEW, Machine Learning, Parallel Testing, Predictive Testing, Python, Test Automation.