On the Use of Artificial Intelligence in Software Testing: State of the Art and Application to Satellite Systems

The integration of Artificial Intelligence (AI) into software testing has emerged as a promising approach to addressing the limitations of conventional methods, particularly in safety-critical domains such as automotive and aerospace. Traditional software testing is often manual, resource-intensive, and prone to incomplete coverage. In contrast, AI-based techniques have been applied to automate test case generation, optimise testing processes, and improve defect prediction. This study extends these approaches to the context of Embodied AI, where the reliability of systems that perceive and act within complex environments is a primary concern. Autonomous satellites are considered as representative embodied agents, for which the robustness of the perception–action loop is mission-critical. The paper presents two main contributions. First, it reviews the state of the art in the application of AI techniques to software testing. Second, it demonstrates the practical viability of these techniques through a proof of concept (PoC) in the satellite domain. In this PoC, generative AI is used to create synthetic datasets simulating adverse atmospheric conditions, including varying levels of cloud opacity. These datasets are used to evaluate and improve an on-board object detection model. Experimental results indicate that this approach reveals limitations of the baseline model and, following retraining, enhances robustness under challenging conditions while maintaining performance in clear scenarios. The findings demonstrate the potential of AI-based testing methodologies to support the validation and improvement of mission-critical embodied systems.