Enhancing Software Requirements Engineering with Language Models and Prompting Techniques: Insights from the Current Research and Future Directions

Moemen Ebrahim, Shawkat Guirguis, Christine Basta

Keywords: LLM, Requirments Engineering, Prompt Engineering, SLM

TL;DR: Insights from the current research on the usage of LLM in the software requirements engineering domain and proposing a conceptual framework to address the challenges and limitations.

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.

Archival Status: Archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 97