Towards AI-centric Requirements Engineering for Industrial Systems

Abstract

Engineering large-scale industrial systems mandate an effective Requirements Engineering (RE) process. Such systems necessitate RE process optimization to align with standards, infrastructure specifications, and customer expectations. Recently, artificial intelligence (AI) based solutions have been proposed, aiming to enhance the efficiency of requirements management within the RE process. Despite their advanced capabilities, generic AI solutions exhibit limited adaptability within real-world contexts, mainly because of the complexity and specificity inherent to industrial domains. This limitation notably leads to the continued prevalence of manual practices that not only cause the RE process to be heavily dependent on practitioners' experience, making it prone to errors, but also often contributes to project delays and inefficient resource utilization. To address these challenges, this Ph.D. dissertation focuses on two primary directions: i) conduct a comprehensive focus group study with a large-scale industry to determine the requirements evolution process and their inherent challenges and ii) propose AI solutions tailored for industrial case studies to automate and streamline their RE process and optimize the development of largescale systems. We anticipate that our research will significantly contribute to the RE domain by providing empirically validated insights in the industrial context.