@inproceedings{ebrahim-etal-2025-enhancing,
title = "Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions",
author = "Ebrahim, Moemen and
Guirguis, Shawkat and
Basta, Christine",
editor = "Zhao, Jin and
Wang, Mingyang and
Liu, Zhu",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-srw.31/",
doi = "10.18653/v1/2025.acl-srw.31",
pages = "486--496",
ISBN = "979-8-89176-254-1",
abstract = "Large Language Models (LLMs) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption. This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically. Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration. We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE. To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chain-of-Verification) to improve output reliability. The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domain-specific prompt templates and hybrid validation pipelines."
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<abstract>Large Language Models (LLMs) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption. This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically. Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration. We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE. To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chain-of-Verification) to improve output reliability. The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domain-specific prompt templates and hybrid validation pipelines.</abstract>
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%0 Conference Proceedings
%T Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions
%A Ebrahim, Moemen
%A Guirguis, Shawkat
%A Basta, Christine
%Y Zhao, Jin
%Y Wang, Mingyang
%Y Liu, Zhu
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-254-1
%F ebrahim-etal-2025-enhancing
%X Large Language Models (LLMs) offer transformative potential for Software Requirements Engineering (SRE), yet critical challenges, including domain ignorance, hallucinations, and high computational costs, hinder their adoption. This paper proposes a conceptual framework that integrates Small Language Models (SLMs) and Knowledge-Augmented LMs (KALMs) with LangChain to address these limitations systematically. Our approach combines: (1) SLMs for efficient, locally deployable requirements processing, (2) KALMs enhanced with Retrieval-Augmented Generation (RAG) to mitigate domain-specific gaps, and (3) LangChain for structured, secure workflow orchestration. We identify and categorize six technical challenges and two research gaps through a systematic review of LLM applications in SRE. To guide practitioners, we distill evidence-based prompt engineering guidelines (Context, Language, Examples, Keywords) and propose prompting strategies (e.g., Chain-of-Verification) to improve output reliability. The paper establishes a theoretical foundation for scalable, trustworthy AI-assisted SRE and outlines future directions, including domain-specific prompt templates and hybrid validation pipelines.
%R 10.18653/v1/2025.acl-srw.31
%U https://aclanthology.org/2025.acl-srw.31/
%U https://doi.org/10.18653/v1/2025.acl-srw.31
%P 486-496
Markdown (Informal)
[Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions](https://aclanthology.org/2025.acl-srw.31/) (Ebrahim et al., ACL 2025)
ACL