A Generic Architecture for Hybrid Intelligent Test Systems

The last decade has obviously witnessed an emerging interest in studying human intelligence aiming to solve complex problems in many industrial fields, e.g. the process of verification and validation. Therefore, in an effort to alleviate the challenges of this process, an idea to construct intelligent test systems from the viewpoints of learning, reasoning, and optimization paradigms is presented. The adopted approach is based on the ability of the test system to observe, model, and store the behavior of skilled human testers aiming to imitate their intelligence. Hence, the developed test system should be able to partially substitute the absence of the human factor during an automated test cycle, which consequently leads to a substantial reduction in the development time and cost; yet the test efficiency is not sacrificed.