Human-in-the-Loop Intelligent Testing for Safety-Critical Software

AI-driven intelligent testing has advanced rapidly, enabling automated test-case generation, defect prediction, and risk assessment. However, the absence of explicit integration of human factors into the testing process often leads to the neglect of testers' cognitive attributes and domain expertise, thereby amplifying cognitive biases and exacerbating safety risks. This paper proposes a three-layer Human-in-the-Loop Intelligent Testing (HITL-IT) framework that systematically incorporates human factors into AI-based testing for safety-critical software. The framework consists of a human-factor modeling layer, an AI testing core, and an interactive feedback loop, collectively forming a closed-cycle mechanism of 'suggestion-challenge-refinement-relearning.' In the context of AI test-case generation, this framework is designed to substantially improve both the quality and efficiency of generated cases. Preliminary applications show promising potential for this approach. By embedding explicit human-factor models and closed-loop feedback into the testing workflow, HITL-IT provides a novel and practical paradigm for building more trustworthy, resilient, and safety-critical AI testing systems.