An Intelligent Requirements Engineering Companion

Abstract

The master thesis investigates the application of dialogue systems in conjunction with large language models (LLMs) to optimize requirements engineering practices. The primary objective is to explore the automation of requirements gathering and translation into technical specifications, reducing manual effort and time consumption. A prototype dialogue system, enriched with in-house company knowledge and augmented by access to a large language model is developed to facilitate this automation process. Moreover, the research delves into the theoretical foundations, methodologies, experiments, and discoveries toward a future where large language models assume a pivotal role in automating and augmenting requirements engineering, potentially eliminating the need for extensive expertise. This is mainly achieved by conducting a semi-systematic literature review and the application of design science research (DSR). Moreover, this research is deeply rooted in the context of addressing the persistent challenges within requirements engineering. The manual, time-consuming requirements elicitation processes, coupled with the complexity and evolving nature of requirements, pose significant hurdles in the project lifecycle. Existing literature underscores the importance of automating these processes, highlighting the potential benefits of AI-driven solutions like dialogue systems integrated with LLMs. However, current tools and techniques lack the emotional, conversational and relational capabilities required for a comprehensive "requirements engineering companion". This thesis steps in to fill this gap by exploring modern technologies to develop a prototype customized for AMAG, serving as an intelligent requirements engineering companion.