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From Specifications to Prompts: On the Future of Generative Large Language Models in Requirements Engineering

Generative LLMs, such as GPT, have the potential to revolutionize Requirements Engineering (RE) by automating tasks in new ways. This column explores the novelties and introduces the importance of precise prompts for effective interactions. Human evaluation and prompt engineering are essential in leveraging LLM capabilities.

Computer Science
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References (10)

01Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic GuidelineAndreas Vogelsang, Jannik Fischbach2024 · arXiv.org · 41 citations In Library
02Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software TraceabilityAlberto D. Rodriguez, Katherine R. Dearstyne, J. Cleland-Huang2023 · 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW) · 71 citations In Library
03Automatically Classifying Kano Model Factors in App ReviewsMichelle Binder, A. Vogt, Adrian Bajraktari et al.2023 · Requirements Engineering: Foundation for Software Quality · 8 citations
04Evaluating classifiers in SE research: the ECSER pipeline and two replication studiesDavide Dell’Anna, Fatma Başak Aydemir, F. Dalpiaz2022 · Empirical Software Engineering · 23 citations
05A Live Extensible Ontology of Quality Factors for Textual RequirementsJulian Frattini, Lloyd Montgomery, Jannik Fischbach et al.2022 · IEEE International Requirements Engineering Conference · 22 citations
06Towards Causality Extraction from RequirementsJannik Fischbach, B. Hauptmann, Lukas Konwitschny et al.2020 · IEEE International Requirements Engineering Conference · 23 citations In Library
07Rapid quality assurance with Requirements SmellsHenning Femmer, Daniel Méndez Fernández, S. Wagner et al.2016 · Journal of Systems and Software · 175 citations
08Automatic Classification of Requirements Based on Convolutional Neural NetworksJ. Winkler, Andreas Vogelsang2016 · 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW) · 118 citations
09BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingJacob Devlin, Ming-Wei Chang, Kenton Lee et al.2019 · North American Chapter of the Association for Computational Linguistics · 119k citations In Library
10Andreas Vogelsang is a full professor of Software Engineering at the University of Cologne, 50923 Cologne, GermanyContact him at vogelsang@cs

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