Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap

• URL: http://arxiv.org/abs/2507.07682v1
• Date: Thu, 10 Jul 2025 12:02:56 GMT
• Title: Prompt Engineering for Requirements Engineering: A Literature Review and Roadmap
• Authors: Kaicheng Huang, Fanyu Wang, Yutan Huang, Chetan Arora,
• Abstract summary: We present the first roadmap-oriented systematic literature review of Prompt Engineering for RE (PE4RE)<n>To bring order to a fragmented landscape, we propose a hybrid taxonomy that links technique-oriented patterns to task-oriented RE roles.
• Score: 7.63638387750336
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities [62.05713042908654]
  This paper provides a review of advances in Large Language Models (LLMs) alignment through the lens of inverse reinforcement learning (IRL)<n>We highlight the necessity of constructing neural reward models from human data and discuss the formal and practical implications of this paradigm shift.
  arXiv  Detail & Related papers  (2025-07-17T14:22:24Z)
• Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering [2.867517731896504]
  generative AI models like Large Language Models (LLMs) have demonstrated their utility across various activities, including within Requirements Engineering (RE)<n> Ensuring the quality and accuracy of LLM-generated output is critical, with prompt engineering serving as a key technique to guide model responses.<n>Existing literature provides limited guidance on how prompt engineering can be leveraged, specifically for RE activities.
  arXiv  Detail & Related papers  (2025-07-04T09:13:50Z)
• Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol [83.90769864167301]
  Literature review tables are essential for summarizing and comparing collections of scientific papers.<n>We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers.<n>Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques.
  arXiv  Detail & Related papers  (2025-04-14T14:52:28Z)
• From Linguistic Giants to Sensory Maestros: A Survey on Cross-Modal Reasoning with Large Language Models [56.9134620424985]
  Cross-modal reasoning (CMR) is increasingly recognized as a crucial capability in the progression toward more sophisticated artificial intelligence systems. The recent trend of deploying Large Language Models (LLMs) to tackle CMR tasks has marked a new mainstream of approaches for enhancing their effectiveness. This survey offers a nuanced exposition of current methodologies applied in CMR using LLMs, classifying these into a detailed three-tiered taxonomy.
  arXiv  Detail & Related papers  (2024-09-19T02:51:54Z)
• Tool Learning with Large Language Models: A Survey [60.733557487886635]
  Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
  arXiv  Detail & Related papers  (2024-05-28T08:01:26Z)
• Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches [2.687757575672707]
  We develop a novel downstream-task taxonomy to perform classification, mapping, and analysis. The main taxonomy requirement is to highlight commonalities while exhibiting variation points of task types.
  arXiv  Detail & Related papers  (2024-04-14T23:45:23Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey [4.362755917924305]
  Large Language Models (LLMs) have shown prominent performance in various downstream tasks. This paper aims to highlight the limitation of designing prompts based on an anthropomorphic assumption.
  arXiv  Detail & Related papers  (2024-01-25T09:47:55Z)
• A Survey on Prompting Techniques in LLMs [0.0]
  Autoregressive Large Language Models have transformed the landscape of Natural Language Processing. We present a taxonomy of existing literature on prompting techniques and provide a concise survey based on this taxonomy. We identify some open problems in the realm of prompting in autoregressive LLMs which could serve as a direction for future research.
  arXiv  Detail & Related papers  (2023-11-28T17:56:34Z)
• Prompts Matter: Insights and Strategies for Prompt Engineering in Automated Software Traceability [45.235173351109374]
  Large Language Models (LLMs) have the potential to revolutionize automated traceability. This paper explores the process of prompt engineering to extract link predictions from an LLM.
  arXiv  Detail & Related papers  (2023-08-01T01:56:22Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.