Efficient Testing of Self-Adaptive Behaviors in Collective Adaptive Systems

Collective adaptive systems (CAS) consist of multiple agents that adapt to changing system and environmental conditions in order to satisfy system goals and quality requirements. As more applications involve using CAS in a critical context, ensuring the correct and safe adaptive behaviors of quality-driven CAS has become more important. In this paper, we propose Collective Adaptive System Testing (CAST), a scalable and efficient approach to testing self-adaptive behaviors of CAS. We propose a selective method to instantiate and execute test cases relevant to the current adaptation context. This enables testers to focus testing on key self-adaptive behaviors while dealing with the scale and dynamicity of the system. An experimental evaluation using a traffic monitoring system is performed to validate its scalability, efficiency, and fault-detection effectiveness. The experimental results provide insights into how CAST can serve as a feasible and effective assurance technique for CAS.