AI-augmented Requirements Engineering for Industrial Systems

Engineering large-scale industrial systems requires an efficient Requirements Engineering (RE) process to manage the complexity resulting from continuous technological advancements. In manufacturing domains such as railways, the complexity of software-intensive systems is growing due to evolving standards, infrastructure specifications, and increasing customer expectations. Typically, the RE process begins with analyzing extensive tender documents from external customers to assess project feasibility. This analysis is critical, as the tender documents define the scope and the standards to which the system-tobe must comply. Once validated and agreed upon, the requirements are distributed among various subsystem teams for development and testing. During implementation, the evolving requirements are cross-referenced with existing technical documents to ensure consistency across project artifacts and prevent integration issues within subsystems. However, the reliance on manual efforts in performing these RE tasks makes the process labor-intensive and timeconsuming, often leading to project scope creep in industrial settings. This thesis empirically investigates Artificial Intelligence (AI), particularly Large Language Models (LLMs)-based solutions, to augment the RE process for realizing complex industrial systems. The proposed solutions aim to provide decision support to reduce the manual efforts typically required for (i) identifying requirements from other supporting information in tender documents, (ii) detecting ambiguous requirements and explaining them, (iii) allocating validated requirements to appropriate subsystem teams for development and (iv) addressing requirement-related queries during the development and release phases of the project. Consequently, this research contributes to enhancing requirements management in complex industrial systems by enabling more efficient and informed decision-making.