An AI-driven Requirements Engineering Framework Tailored for Evaluating AI-Based Software

Requirements Engineering (RE) has been extensively refined for traditional software systems, but AI-based software (AIS)<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>In this work, AI-based software (AIS) refers to software that relies exclusively on vision-based perception, meaning its understanding of the environment is derived solely from camera input. introduces unique challenges that necessitate novel approaches. This paper addresses the gap in RE practices for AIS by proposing a framework that leverages partial specifications of domain concepts from RE and employs eXplainable AI (XAI) to verify AIS's perception of these specifications. The purpose of this framework is to demonstrate that systematically engineering AIS, according to RE practices, rather than fully relying on AI capabilities, will enhance the perception capabilities of resultant AIS. This work aims to enhance RE4AI by offering a structured approach for managing and evaluating requirements specifications in AIS, ultimately leading to improved performance in these systems. Evaluation results showed that our framework improves AIS perception of variants of two domain concepts-pedestrian and aircraft-within the automotive and aviation domains.