Towards a Generative AI-Driven Environment for the Specification and Evaluation of IoT Software Systems Requirements

Abstract

IoT software systems involve implicit and explicit interactions among humans, devices, and environments, which makes requirements specification and evaluation more complex and context-sensitive. Large Language Models (LLMs) emerge as potential tools to support Requirements Engineering (RE) activities, although empirical evidence on their application in IoT software systems remains limited. In this article, we analyze how Generative Artificial Intelligence (GenAI) tools can support the specification and quality evaluation of requirements in IoT software systems through a two-stage application study. First, we carried out a Rapid Review of the Scopus database to identify GenAI tools used in RE. Next, we conducted an experimental study in a real-world setting to develop a smart home system to support a person with physical disabilities. We used ChatGPT to generate (i) requirements from a vision document and a predefined specification structure, and (ii) the evaluation of the requirements against ISO/IEC/IEEE 29148 criteria. The model generated an initial specification containing 35 requirements (23 functional and 12 non-functional) and provided suggestions for improvement during the quality evaluation phase. Only a subset of the requirements required additional adjustments by the engineers. The results indicate that GenAI tools have the potential to support the initial generation and review of requirements, reducing the initial documentation effort. However, supervision by requirements engineers remains essential. As an additional contribution, this study proposes the SpecEval-IoT environment to automate the specification and evaluation of requirements for IoT software systems.