pith. sign in

arxiv: 2507.07682 · v1 · pith:UG6CINO4new · submitted 2025-07-10 · 💻 cs.SE

Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap

classification 💻 cs.SE
keywords engineeringpromptllmscurrentliteraturerequirementsresearchreview
0
0 comments X
read the original abstract

Advancements in large language models (LLMs) have led to a surge of prompt engineering (PE) techniques that can enhance various requirements engineering (RE) tasks. However, current LLMs are often characterized by significant uncertainty and a lack of controllability. This absence of clear guidance on how to effectively prompt LLMs acts as a barrier to their trustworthy implementation in the RE field. We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE). Following Kitchenham's and Petersen's secondary-study protocol, we searched six digital libraries, screened 867 records, and analyzed 35 primary studies. To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns (e.g., few-shot, Chain-of-Thought) to task-oriented RE roles (elicitation, validation, traceability). Two research questions, with five sub-questions, map the tasks addressed, LLM families used, and prompt types adopted, and expose current limitations and research gaps. Finally, we outline a step-by-step roadmap showing how today's ad-hoc PE prototypes can evolve into reproducible, practitioner-friendly workflows.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification

    cs.SE 2026-07 unverdicted novelty 6.0

    BT-APE automates prompt engineering for requirements classification using backtracking search and dynamic examples, matching PE2 accuracy while using 72% fewer tokens and 66% less time than that baseline.