REFSQ 2024
Mon 8 - Thu 11 April 2024 Winterthur, Switzerland
Tue 9 Apr 2024 11:40 - 12:00 at Vorhangsaal Conference room MA-E0.46 - AI for RE (R2) Chair(s): Fabiano Dalpiaz

In this paper, we address the question of whether general-purpose AI-based tools may be useful for detecting variability in Natural Language (NL) requirements documents. For this purpose, we consider GPT-3.5, the Generative Pretrained Transformer language model developed by OpenAI, and Microsoft Bing in creative mode. Using two exemplar NL requirements documents, we compare their variability detection capability with that of experts and that of a rule-based NLP tool.

Tue 9 Apr

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

11:00 - 12:30
AI for RE (R2)Research Track at Vorhangsaal Conference room MA-E0.46
Chair(s): Fabiano Dalpiaz Utrecht University
A tertiary study on AI for Requirements EngineeringScientific evaluationBest Paper Candidate
Research Track
P: Ali Mehraj Tampere University, A: Zheying Zhang Tampere University, A: Kari Systa Tampere University, D: Laura Semini Università di Pisa - Dipartimento di Informatica
Exploring LLMs' ability to detect variability in requirementsResearch Preview
Research Track
A: Alessandro Fantechi University of Florence, A: Stefania Gnesi Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" , P: Laura Semini Università di Pisa - Dipartimento di Informatica, D: Jill Tamanini Fraunhofer IESE
Opportunities and Limitations of AI in Human-Centered Design - A Research PreviewResearch Preview
Research Track
A: Anne Hess Technical University of Applied Sciences Würzburg-Schweinfurt / Fraunhofer IESE, A: Thomas Immich Centigrade GmbH, P: Jill Tamanini Fraunhofer IESE, A: Mario Biedenbach Fraunhofer IESE, A: Matthias Koch Fraunhofer IESE, D: Ali Mehraj Tampere University