Testing of Cyber-Physical Systems Having an AI-Based Component

Abstract

Cyber-Physical Systems (CPS) integrate computational and physical processes, with applications spanning automotive, industrial robotics, and home automation industries. As CPS becomes more intricate due to technological advancements, the need for robust development and testing methodologies to ensure reliability and safety has become paramount. Traditional software development models are often insufficient for managing the combined hardware, software, and network complexities characteristic of CPS. This research introduces an extended V-Model for system engineering tailored to the development and testing of CPS. Our model adapts and expands upon the traditional V-Model used in software engineering, incorporating modern techniques such as A/B/n testing and Explainable AI (XAI). This methodology enables parallel development and testing processes. The V-Model begins with a requirement specification that outlines the CPS profile, including sensor types, network architecture, and computational needs. The functional specification phase assesses system responses under various conditions, ensuring the expected functionality is met. The system is divided into its core components in the architectural design phase: software, hardware, and network infrastructure. This stage prepares the system for implementation, integrating sensor data acquisition, data transfer protocols, and AI analytical modules. For unit testing, mutation testing is employed using mutation operators to simulate potential system faults. This enhances system robustness by ensuring the AI model can handle failures related to hardware, software, or network issues. Fault seeding helps to identify vulnerabilities within the AI, particularly in Neural Networks, Random Forest, Gradient Boosting, and other algorithms used depending on the specific case. Integration testing incorporates A/B/n testing with combinatorial logic to evaluate different CPS configurations. This approach compares variants under real-world conditions, helping to identify the optimal setup for performance and reliability. In system testing, the fault model is employed to ensure coverage across hardware, software, network, and environmental conditions. Test cases are designed to capture the full range of potential faults that could impact the CPS. Explainable AI techniques, such as SHAP (Shapley Additive Explanations), are used to interpret the AI model's predictions during system testing, providing insights into CPS behavior in different scenarios. Additionally, real-time alerts are generated based on the AI’s predictions of CPS performance. The final phase involves acceptance testing, where the system’s performance is validated in the target environment against predefined project requirements. Mutation testing further strengthens the system’s reliability by identifying areas of potential failure, ensuring the CPS is protected against a wide range of possible issues. The proposed extended V-Model provides a comprehensive approach to CPS development, covering the relationships between hardware, software, networks, and AI algorithms. By integrating modern testing strategies such as A/B/n testing and mutation testing, this model enhances the reliability, security, and efficiency of CPS. Future work will extend the applicability of this model to other domains and address emerging challenges in CPS development.