Requirements Engineering Using Generative AI: Prompts and Prompting Patterns

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

Learning to detect unseen object classes by between-class attribute transfer

Lampert, C. H., Nickisch, H., & Stefan Harmeling, B. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206594 · Cited by 1,563 · see citing works

Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · see citing works

A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023) · arXiv · doi:10.48550/arxiv.2302.11382 · Cited by 838 · see citing works

Design Guidelines for Prompt Engineering Text-to-Image Generative Models

Liu, V., & Chilton, L. B. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501825 · Cited by 639 · see citing works

Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.556 · Cited by 610 · see citing works

ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

White, J., Hays, S., Fu, Q., Spencer-Smith, J. B., & Schmidt, D. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-55642-5_4 · Cited by 344 · see citing works

An empirical evaluation of GitHub copilot's code suggestions

Nguyen, N., & Nadi, S. (2022) · Proceedings of the 19th International Conference on Mining Software Repositories · doi:10.1145/3524842.3528470 · Cited by 313 · see citing works

PURE: A Dataset of Public Requirements Documents

Ferrari, A., Spagnolo, G. O., & Gnesi, S. (2017) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2017.29 · Cited by 207 · see citing works

Zero-shot learning for requirements classification: An exploratory study

Alhoshan, W., Ferrari, A., & Zhao, L. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107202 · Cited by 109 · see citing works

Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting

Zhang, J., Chen, Y., Liu, C., Niu, N., & Wang, Y. (2023) · International Conference on Intelligent Computing and Next Generation Networks (ICNGN) · doi:10.1109/icngn59831.2023.10396810 · Cited by 55 · see citing works

ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software DesignPreprint

White, J., Hays, S., Fu, Q., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2303.07839 · Cited by 39 · see citing works

Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming

Fiannaca, A. J., Kulkarni, C. E., Cai, C. J., & Terry, M. A. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3585737 · Cited by 35 · see citing works

Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains

Cheng, Y., Chen, J., Huang, Q. Y., Xing, Z., Xu, X., & Lu, Q. (2023) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3638247 · Cited by 35 · see citing works

Nfr

Cleland‐Huang, J., Mazrouee, S., Liguo, H., & Port, D. N. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.268542 · Cited by 26 · see citing works

Panel: Context-Dependent Evaluation of Tools for NL RE Tasks: Recall vs. Precision, and Beyond

Berry, D. M., Cleland‐Huang, J., Ferrari, A., Maalej, W., Mylopoulos, J., & Zowghi, D. (2017) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2017.64 · Cited by 18 · see citing works

A systematic mapping study on machine learning methodologies for requirements management

Xu, C. Y., Li, Y., Wang, B., & Dong, S. (2022) · IET Software · doi:10.1049/sfw2.12082 · Cited by 9 · see citing works

Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot SettingPreprint

Zhang, J., Chen, Y., Niu, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.12562 · Cited by 4 · see citing works

Large Language Models lack essential metacognition for reliable medical reasoning

Griot, M., Hemptinne, C., Vanderdonckt, J., & Yuksel, D. (2025) · Nature Communications · doi:10.1038/s41467-024-55628-6 · Cited by 113 · see citing works

Model Generation with LLMs: From Requirements to UML Sequence Diagrams

Ferrari, A., Abualhaija, S., & Arora, C. (2024) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew61692.2024.00044 · Cited by 105 · see citing works

Research directions for using LLM in software requirement engineering: a systematic review

Hemmat, A., Sharbaf, M., Kolahdouz-Rahimi, S., Lano, K., & Tehrani, S. Y. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1519437 · Cited by 62 · see citing works

Can generative artificial intelligence productivity tools support workplace learning? A qualitative study on employee perceptions in a multinational corporation

Callari, T. C., & Puppione, L. (2025) · Journal of Workplace Learning · doi:10.1108/jwl-11-2024-0258 · Cited by 38 · see citing works

Prompt Literacy

Maloy, R. W., & Gattupalli, S. (2024) · doi:10.59668/371.14442 · Cited by 20 · see citing works

Promptology: enhancing human-AI interaction in large language models

Olla, P., Elliott, L., Abumeeiz, M., Mihelich, K., & Olson, J. (2024) · Information · doi:10.3390/info15100634 · Cited by 17 · see citing works

Assisting early-stage software startups with LLMs: Effective prompt engineering and system instruction design

Ahlgren, T. L., Sunde, H. F., Kemell, K., & Nguyen‐Duc, A. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107832 · Cited by 15 · see citing works

Journalistic Agency and Power in the Era of Artificial Intelligence

Thäsler-Kordonouri, S., & Koliska, M. (2025) · Journalism Practice · doi:10.1080/17512786.2025.2480238 · Cited by 13 · see citing works

What do professional software developers need to know to succeed in an age of Artificial Intelligence?

Kam, M., Miller, C., Wang, M., Tidwell, A., Lee, I. A., Malyn‐Smith, J., Perret, B., Tiwari, V., Kenitzer, J., Macvean, A., et al. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3727251 · Cited by 11 · see citing works

Reflexive Prompt Engineering

Djeffal, C. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732118 · Cited by 10 · see citing works

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

Ronanki, K., Arvidsson, S., & Axell, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04207-1_17 · Cited by 10 · see citing works

Quest-RE QUestion Generation and Exploration STrategy for Requirements Engineering

Hasso, H., Fischer-Starcke, B., & Geppert, H. (2024) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew61692.2024.00006 · Cited by 9 · see citing works

Unraveling the pain points of domain modeling

Sousa, I. V., Sales, T. P., Guerra, E., Daneva, M., Guizzardi, R., Santos, L. O. B. D. S., Proper, H. A., & Guizzardi, G. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107736 · Cited by 7 · see citing works

Promptology: Enhancing Human-AI Interaction in Large Language ModelsPreprint

Olla, P., Elliott, L., & Abumeeiz, M. (2024) · Preprints.org · doi:10.20944/preprints202408.0334.v1 · Cited by 7 · see citing works

Ensuring Integrity in AI Research: Frameworks for Assessing Validity and Reliability of GenAI Outputs

Poornesh, M. (2025) · The Clearing House A Journal of Educational Strategies Issues and Ideas · doi:10.1080/00098655.2025.2493717 · Cited by 6 · see citing works

Artificial intelligence in soil science: Where do we go now?

Castro, J. P., Gasch, C. K., & Flores, P. (2024) · Agricultural & Environmental Letters · doi:10.1002/ael2.20134 · Cited by 7 · see citing works

Data-Centric Fine-Tuning of Small Language Models for Automatic Extraction of Technical Requirements

Müller, L., Schwarz, N., Böcking, L., Bereczuk, A., Stagge, H., Kratsch, W., & Kühl, N. (2025) · IEEE Access · doi:10.1109/access.2025.3591739 · Cited by 5 · see citing works

Exploring Multi-Label Data Augmentation for LLM Fine-Tuning and Inference in Requirements Engineering: A Study with Domain Expert Evaluation

Liu, H., García, M. B., & Korkakakis, N. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00064 · Cited by 5 · see citing works

Evidências sobre o uso do ChatGPT no ensino de modelagem de software: um experimento controlado

Murad, S. B., Lemos, F. F. S., Melo, S. M., Paschoal, L. N., & Prates, J. M. (2025) · Anais do Workshop sobre Educação em Computação (WEI) · doi:10.5753/wei.2025.9261 · Cited by 4 · see citing works

ReqNet: an LLM-driven computational framework for automated requirements extraction from unstructured documents

Saleem, S., Asim, M. N., & Dengel, A. R. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02143-w · Cited by 4 · see citing works

Generative AI and Conceptual Modeling

Fill, H.-G., Horkoff, J., Fettke, P., & Köpke, J. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00978-8 · Cited by 4 · see citing works

Aligning Data Debt with AI-Integrated Software Project Lifecycle Processes: A Standard-Based Mapping Approach

Akgül, N. Y., Temizel, T. T., Özcan‐Top, Ö., & Akman, P. D. (2025) · IEEE/ACM International Conference on Technical Debt (TechDebt) · doi:10.1109/techdebt66644.2025.00008 · Cited by 4 · see citing works

Muli-Agent LLMs as Ethics Advocates for AI based Systems

Yamani, A. Z., Baslyman, M., & Ahmed, M. (2025) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew66121.2025.00078 · Cited by 4 · see citing works

On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software

Nouri, A., Andersson, J., Hornig, K. D. J., Fei, Z., Knabe, E., Sivencrona, H., Cabrero‐Daniel, B., & Berger, C. (2025) · International Conference on Evaluation and Assessment in Software Engineering (EASE) · doi:10.1145/3756681.3756987 · Cited by 6 · see citing works

Cases and Lessons in Prompting Artificial Intelligence for Social Work

Fricker, S., Goldkind, L., Hackstein, S., & Weber, J. (2025) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew66121.2025.00087 · Cited by 3 · see citing works

Prompts as Software Engineering Artifacts: A Research Agenda and Preliminary Findings

Villamizar, H., Fischbach, J., Korn, A., Vogelsang, A., & Méndez, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-12089-2_32 · Cited by 3 · see citing works

Towards Multi-Agent Systems in Requirements Engineering: A Proof-of-Concept Study Using Large Language Models

Lai, T. Y. E., Allende‐Cid, H., Giesselbach, S., & Stefan Rüping, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21480-5_8 · Cited by 2 · see citing works

Classification of Crowd-Based Software Requirements via Unsupervised Learning

SHARMA, N. K., & Sharma, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70245-7_17 · Cited by 2 · see citing works

Generative AI for Smart Contracts in Real Estate Business

Zhao, W., Pobbathi, L., Ramprasath, S., & Patibandla, M. (2024) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa63982.2024.00018 · Cited by 2 · see citing works

FeReRe: Feedback Requirements Relation Using Large Language Models

Anders, M., & Paech, B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88531-0_7 · Cited by 2 · see citing works

How Effective are Generative Large Language Models (LLMs) in Performing Requirements Classification? Assessing the Impact of LLMs, Prompts, Datasets, and TasksPreprint

Alhoshan, W., Ferrari, A., & Zhao, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5472929 · Cited by 2 · see citing works

Exploring Generative Pretrained Transformers to Support Sustainability Effect Identification - A Research Preview

Paech, B., Kaiser, P., Bambazek, P., Groher, I., & Seyff, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88531-0_16 · Cited by 1 · see citing works

The Engineering of AI Evaluation and Scoring: Overview and Insights

Sohier, H., Faure, J.-P., Hallensleben, S., Streiff, P., Creff, S., Hien, E., Mattioli, J., Odabas, S., & Vernhes, R. (2025) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon64521.2025.11014820 · Cited by 1 · see citing works

An Approach to Developing a Software Requirements Specification Template for Greenfield Projects Using Artificial Intelligence

Navrotskyi, M., Gordieiev, O., & Gordieieva, D. (2024) · International Conference on Dependable Systems, Services and Technologies (DESSERT) · doi:10.1109/dessert65323.2024.11122187 · Cited by 1 · see citing works

From ConOps to an Operational Domain Model: Harnessing LLMs for Conceptual Model Design

Gupta, S., Rezayat, S., Burmester, G., Durak, U., Ma, H., & Hartmann, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08620-4_9 · Cited by 1 · see citing works

Transforming Natural Language Requirements to Formalism Using LLMs

Meng, B., Lorch, R., Siu, K., Durling, M., Varanasi, S. C., Paul, S., & Moitra, A. (2025) · Systems Engineering · doi:10.1002/sys.70023 · Cited by 1 · see citing works

Generative Artificial Intelligence for Requirements Engineering in Software Development – Analysis of the State-of-the-Art

Lang, J., & Fischer, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13187-4_12 · Cited by 1 · see citing works

Enhancing Security Requirements Engineering for IoT Smart Homes: A BERT-based Approach to Automate completeness checking

Janisar, A. A., Meidan, A., Kalid, K. S., Sarlan, A. B., Gilal, A. R., Maiwada, U. D., & Faheem, Z. B. (2026) · Scientific African · doi:10.1016/j.sciaf.2026.e03275 · Cited by 1 · see citing works

Going with the Mainstream: Exploring GPT Representation of Journalistic Culture

Kang, T., Vos, T. P., Hanitzsch, T., Thurman, N., Henkel, I., Thäsler-Kordonouri, S., & Loosen, W. (2026) · The International Journal of Press/Politics · doi:10.1177/19401612261442761 · Cited by 1 · see citing works

From issue titles to requirements: an empirical study of large language models and prompt engineering strategies

Paiva, G. P., Canedo, E. D., & Filho, G. P. R. (2026) · Requirements Engineering · doi:10.1007/s00766-026-00462-z · Cited by 1 · see citing works

Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework

Wang, C., Chen, Z.-P., Li, T.-L., Zhang, Y.-L., & Liu, Y. (2024) · 2025 IEEE/ACM 47th International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1109/icse-nier66352.2025.00017 · Cited by 8 · see citing works

Extraction of App Problems and its Corresponding User Action from User Review of Apps Using Few-Shot LearningPreprint

Eftee, S. Y., Khan, M. Y., Noor, R., Mahmud, H., & Hasan, M. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5093717

Evaluating Large Language Models for the Automated Generation of Software Requirements

Puchleitner, T., Lubos, S., Felfernig, A., & Garber, D. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8892-0_37

Exploring the Use of Generative AI in Software Development: A Preliminary Study

Mar’i, F., Nerisafitra, P., Marianingsih, S., Hanjaya, S., Aisyah, Y., Primatama, D., & Sutaji, D. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202564504002

Leveraging LLMs for Requirements Engineering Education: How to Approach?

Tiwari, S., & Rathore, S. S. (2025) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re63999.2025.00055

One Size Does Not Fit All: On the Role of Batch Size in Classifying Requirements with LLMs

van Can, A. T., Aydemir, F. B., & Dalpiaz, F. (2025) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew66121.2025.00009

PF4SAFE: Problem Frames extension for Safety Analysis based on FBN Evaluation

Zhang, L., Xiao, H., Wang, W., Li, Z., & Li, C. (2025) · IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) · doi:10.1109/qrs-c65679.2025.00049

Experimental Evaluation of AI-Augmented Cybersecurity Requirements Generation Leveraging LLMs’ Capabilities

Yelmo, J. C., Martín, Y.-S., & Perez-Acuna, S. (2026) · IEEE Access · doi:10.1109/access.2026.3658339

Accelerating Requirements Specification Using Large Language Models and Retrieval Augmented Generation – A Case Study

Salas, F. L., Gajek, F., & Busboom, A. (2026) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418833

Utilizing Chain of Thought to Generate Code from Challenging Programming Requirements

EmanAlofi, RehabIbrahim, & AmnehAlAbdi (2026) · International Conference on Consumer Electronics · doi:10.1109/icce67443.2026.11449835

Requirements Engineering with Generative AI: Improving Requirements Development Efficiency with Structured Prompt Technique (SPT)

Nayeem, K., Demir, K. A., Sekharan, C. N., & Liu, B. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468132

Extending the Comparative Study of Anomaly Detection Tools in Software Requirements with ChatGPT

Pereira, F. R., Costa, H. A. X., & Júnior, P. A. P. (2026) · Journal of Software Engineering Research and Development · doi:10.5753/jserd.2026.5919

Generative AI for User Story Writing and Refinement in Software Engineering Courses

Ataby, N., Ibrahim, N., Seyam, M., & Abu-Elkheir, M. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574430

An Exploratory Study on Requirements Reuse: Comparing Manual and ChatGPT-Based Approaches

Oliveira, R. A., Carvalho, R. M., Cardoso, J. C. P., Gurgel, J. L., Santos, I. D. S., & Andrade, R. M. C. (2026) · doi:10.1145/3786182.3788309

What Resources Are Available for Parents Online? A Readability, Quality, and Source Evaluation of Website and AI-Generated Content on Inherited Pediatric Orthopaedic Conditions

Hayes, D. S., Blank, E., & Muchow, R. D. (2026) · Journal of the Pediatric Orthopaedic Society of North America · doi:10.1016/j.jposna.2026.100420

An Analysis of Student Perceptions and Learning Impact of Large Language Models in Requirements Engineering Education

Karimi, M. A., Tiwari, S., & Rathore, S. S. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3805784

A Systematic Review of Artificial Intelligence for Software Requirements in Open-Source Software

Alharbi, A., & Slhoub, K. (2026) · IEEE Access · doi:10.1109/access.2026.3715394

AXL: Executable Intent for Governed LLM-Based Software Maintenance

Vella, S., Sharieh, S., Ferworn, A., & Sharieh, M. (2026) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece68150.2026.11610535

إطار عمل FAIRE: بنية رسمية للمتطلبات الناشئة في هندسة البرمجيات المعززة بالذكاء الاصطناعي

عبدالسلام, ع. ع. (2026) · مجلة غريان للتقنية · doi:10.66660/ghyn.v1i12.131

AI assisted requirements engineering for system development

Oceguera, C. M., & Salas, C. H. (2026) · CognitivaTech Ingeniería de Software Inteligente y Sistemas Adaptativos · doi:10.63688/cognitivatech202652

Integrating Generative AI with Lean UX for Knowledge Engineering

dos Santos, R. A., Gauthier, F. Á. O., Macedo, M., & Roberg, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-23684-5_38

Uneven by Design: How Generative AI Interfaces Reinforce Digital Divides Across Modalities

Jung, S., Han, S., Yoo, G. Y., Pasch, S., Ji, Y. G., Kim, H. C., & Cha, M. C. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2719091

Debugging Requirements Interview Scripts: A Framework for Quality Assessment

Tefur, A. E.-M., Fotrousi, F., Ferrari, A., Spoletini, P., & Maalej, W. (2026) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re68928.2026.00007

Is that Really You? Understanding Persona Adoption in LLM-Based Requirements Engineering

Morente, P., Motger, Q., & Oriol, M. (2026) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re68928.2026.00052

Reframing Software Architecture and Design Skills in the Era of Generative Artificial Intelligence

Codabux, Z., & Galster, M. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3774748.3795868

Hey, ChatGPT, Look at My Work: Using Conversational AI in Requirements Engineering Education

Badihi, S., Tegegn, M., Riddell, E., Czarnecki, K., & Rubin, J. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3773112

Executable Intent: Making LLM Workflows Enterprise-Ready with Domain-Specific Languages

Vella, S., Sharieh, S., & Ferworn, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32729-1_5

Classifying Conversations from Real World Software Projects: An Analysis of Five Different Zero-Shot Prompts

Assumpção, C. M., Siqueira, F. L., Leite, J. C. S. D. P., & Barguil, J. M. M. (2026) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew72749.2026.00040

Artificial Intelligence prompt engineering in tourism and hospitality: a task-technology fit theory on performance, complexity, and user literacy

Saleh, M. I., & Sthapit, E. (2026) · Tourism Recreation Research · doi:10.1080/02508281.2026.2727979

Automatic Prompt Engineering: The Case of Requirements Classification

Zadenoori, M. A., Zhao, L., Alhoshan, W., & Ferrari, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88531-0_15 · Cited by 7 · see citing works

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements ClassificationPreprint

Zadenoori, M. A., Alhoshan, W., Dąbrowski, J., Zhao, L., & Ferrari, A. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2607.00427

From Specifications to Prompts: On the Future of Generative Large Language Models in Requirements Engineering

Vogelsang, A. (2024) · IEEE Software · doi:10.1109/ms.2024.3410712 · Cited by 30 · see citing works

Prompt engineering guidelines for LLMs in Requirements Engineering

Arvidsson, S., & Axell, J. (2023) · Gothenburg University Publications Electronic Archive (Gothenburg University)

Prompt Engineering Guidelines for Using Large Language Models in Requirements Engineering

Ronanki, K., Arvidsson, S., & Axell, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04207-1_17 · Cited by 10 · see citing works