Enhancing Industrial Requirements Processing and Reuse

We live in a world that depends on software. From the moment we log in to a banking system or when we take the bus to work, we are surrounded by software-intensive systems. These systems are often not built from scratch, but as further iterations of existing systems, adapted for different customers and market segments.The development of such complex software and variant-intensive systems is centered around customer needs that are usually described in long documents, full of detail, and written in natural language. Companies must read through, interpret, and extract the relevant requirements, decide which teams should develop and test them, and simultaneously identify what can be reused from earlier projects. This process is often manual, carries a risk of mistakes, and demands great experience and precision.This thesis explores how Artificial Intelligence (AI), and in particular natural language processing (NLP), can help make the process both faster and more reliable. The work is based on six scientific articles, which make four contributions, as follows. First, we study how requirements management and reuse are handled today to identify opportunities for enhancement. Next, we focus on automating the identification and allocation of requirements, so that correct requirements are identified and directed to the right teams from the start. We also develop methods for discovering which parts of previous projects can be reused, to avoid redundant development efforts. Finally, we create a pedagogical resource that enables teachers, students, and professionals to apply the technical solutions in practice.Through these contributions, the thesis demonstrates how AI can become a powerful support in processing requirements and supporting reuse in complex software development.