Conference Agenda
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Agenda Overview |
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D332: AI-AUGMENTED REQUIREMENTS ENGINEERING
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AI applications in requirements engineering: a systematic mapping study 1IPEK - Institute of Product Engineering, Karlsruhe Institute of Technology, Germany; 2FAPS - Institute for Factory Automation and Production Systems, Friedrich-Alexander Universität Erlangen-Nürnberg, Germany Artificial intelligence influences requirements engineering, but it remains unclear which activities benefit and how. This paper reviews 15 studies from the last five years, classifying AI approaches with an established RE framework. Current work focuses on operational tasks: requirements determination, analysis, consolidation, and traceability. About two thirds address single activities rather than integrated solutions. Early-phase tasks like knowledge elicitation receive little support despite being central to practice. The mapping clarifies existing AI support and gaps for future work. An LLM model to guide and enrich the understanding of stakeholder value and development of product requirements Carnegie Mellon University, United States of America Leveraging the vast interconnection of language and ideas through Large Language Models, a designer’s understanding of the needs, wants and desires of intended stakeholders defines the value proposition and product design requirements of a product or service through implementation of the Value Opportunity Analysis (VOA). The resulting VOA LLM Bot explores emotion, aesthetic and other human-valued attributes, and significantly increases perception of the VOA as a useful method for identifying product requirements, and analyzing opportunity solutions. Context-aware large language models for ambiguity detection in requirements 1University of Technology Sydney, Australia; 2Swinburne University of Technology, Australia Requirements quality shapes engineering design, yet natural language specifications remain vulnerable to ambiguity. We investigate how LLMs support ambiguity detection using a hybrid dataset combining NASA JWST requirements with systematically injected defects. Using auto-extracted domain knowledge, we compare a domain-agnostic baseline with a context-aware approach. Incorporating domain knowledge helps LLMs better distinguish genuinely ambiguous requirements from acceptable ones, highlighting the potential of context-aware AI assistants for requirements engineering and early-stage design. Enabling AI-supported requirements engineering through model-based systems engineering and characteristics-properties modeling Technical University of Darmstadt, Germany High-quality requirements are essential for successful product development. This work proposes a model-based requirements engineering framework and AI-supported tool. The framework links design characteristics and measured properties via an OPM-based system model. This enables the implementation of a tool for systematic verification and validation of requirements in early product development stages, supporting the transition from experience- to data-/evidence-driven decision making and industry 4.0 paradigms. A hydraulic-press case-study demonstrates feasibility of the end-to-end workflow. | ||
