Automating the Detection of Requirement Dependencies Using Large Language Models

[2602.22456] Automating the Detection of Requirement Dependencies Using Large Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2602.22456 (cs) [Submitted on 25 Feb 2026 ( v1 ), last revised 14 Aug 2026 (this version, v2)] Title: Automating the Detection of Requirement Dependencies Using Large Language Models Authors: Ikram Darif , Feifei Niu , Manel Abdellatif , Lionel C. Briand , Ramesh S. , Arun Adiththan View a PDF of the paper titled Automating the Detection of Requirement Dependencies Using Large Language Models, by Ikram Darif and 4 other authors View PDF HTML (experimental) Abstract: Requirements are inherently interconnected through various types of dependencies. Identifying these dependencies is essential, as they underpin critical decisions and influence a range of activities throughout software development. However, this task is challenging, particularly in modern software systems, given the high volume of complex, coupled requirements. These challenges are further exacerbated by the ambiguity of Natural Language (NL) requirements and their constant change. Consequently, requirement dependency detection is often overlooked or performed manually. Large Language Models (LLMs) exhibit strong capabilities in NL processing, presenting a promising avenue for requirement-related tasks. While they have shown to enhance various requirements engineering tasks, their effectiveness in identifying requirement dependencies remains unexplored. In this paper, we introduce LEREDD, an LLM-based approach for automated detection of requirement dependencies that leverages Retrieval-Augmented Generation (RAG) and In-Context Learning (ICL). It is designed to identify diverse dependency types directly from NL requirements. We empirically evaluate LEREDD against two state-of-the-art baselines. The results show that LEREDD provides highly accurate classification of dependent and non-dependent requirements, achieving an accuracy of 0.93, and an F1 score of 0.84, with the latter averaging 0.96 for non-dependent cases. LEREDD outperforms zero-shot LLMs and baselines, particularly in detecting fine-grained dependency types, where it yields average relative gains of 94.87% and 105.41% in F1 scores for the Requires dependency over the baselines. We also provide an annotated dataset of requirement dependencies encompassing 813 requirement pairs across three distinct systems to support reproducibility and future research. Subjects: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.22456 [cs.SE] (or arXiv:2602.22456v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2602.22456 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ikram Darif [ view email ] [v1] Wed, 25 Feb 2026 22:33:27 UTC (350 KB) [v2] Fri, 14 Aug 2026 22:30:36 UTC (295 KB) Full-text links: Access Paper: View a PDF of the paper titled Automating the Detection of Requirement Dependencies Using Large Language Models, by Ikram Darif and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2026-02 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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