# Generative AI for Requirements Engineering: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2409.06741 · HTML: https://mlsanity.com/paper/2409.06741/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 106 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [Artificial Intelligence: the global landscape of ethics guidelines](https://mlsanity.com/paper/1906.11668/) (2019)
- [Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations](https://mlsanity.com/paper/2310.11207/) (2023)
- [Towards Robust and Privacy-preserving Text Representations](https://mlsanity.com/paper/1805.06093/) (2018)
- [Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks](https://mlsanity.com/paper/2102.02017/) (2021)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains](https://mlsanity.com/paper/2306.12028/) (2023)
- [Generative Artificial Intelligence for Software Engineering -- A Research Agenda](https://mlsanity.com/paper/2310.18648/) (2023)
- [Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair](https://mlsanity.com/paper/2008.02944/) (2020)
- [Requirements are All You Need: From Requirements to Code with LLMs](https://mlsanity.com/paper/2406.10101/) (2024)
- [An Experiment in Retrofitting Competency Questions for Existing Ontologies](https://mlsanity.com/paper/2311.05662/) (2023)

## Built on by (most cited first, 11 of 11)

- [DRAFT-ing Architectural Design Decisions using LLMs](https://mlsanity.com/paper/2504.08207/) (2025)
- [Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study](https://mlsanity.com/paper/2503.15248/) (2025)
- [Can LLMs Generate User Stories and Assess Their Quality?](https://mlsanity.com/paper/2507.15157/) (2025)
- [SEER: Sustainability Enhanced Engineering of Software Requirements](https://mlsanity.com/paper/2510.08981/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)
- [AI for Requirements Engineering: Industry adoption and Practitioner perspectives](https://mlsanity.com/paper/2511.01324/) (2025)
- [QUARE: Quality-Aware Requirements Analysis through Multi-Agent Dialectical Negotiation](https://mlsanity.com/paper/2603.11890/) (2026)
- [Towards Energy-aware Requirements Dependency Classification: Knowledge-Graph vs. Vector-Retrieval Augmented Inference with SLMs](https://mlsanity.com/paper/2603.23954/) (2026)
- [ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation](https://mlsanity.com/paper/2604.23124/) (2026)
- [Integrating AI into Requirements Quality Learning in Software Engineering Education: A TPACK-Guided Empirical Study](https://mlsanity.com/paper/2607.28176/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Introduction: Requirements engineering faces challenges due to the handling of increasingly complex software systems. These challenges can be addressed using generative AI. Given that GenAI based RE has not been systematically analyzed in detail, this review examines related research, focusing on trends, methodologies, challenges, and future directions. Methods: A systematic methodology for paper selection, data extraction, and feature analysis is used to comprehensively review 238 articles published from 2019 to 2025 and available from major academic databases. Results: Generative pretrained transformer models dominate current applications (67.3%), but research remains unevenly distributed across RE phases, with analysis (30.0%) and elicitation (22.1%) receiving the most attention, and management (6.8%) underexplored. Three core challenges: reproducibility (66.8%), hallucinations (63.4%), and interpretability (57.1%) form a tightly interlinked triad affecting trust and consistency. Strong correlations (35% cooccurrence) indicate these challenges must be addressed holistically. Industrial adoption remains nascent, with over 90% of studies corresponding to early stage development and only 1.3% reaching production level integration. Conclusions: Evaluation practices show maturity gaps, limited tool and dataset availability, and fragmented benchmarking approaches. Despite the transformative potential of GenAI based RE, several barriers hinder practical adoption. The strong correlations among core challenges demand specialized architectures targeting interdependencies rather than isolated solutions. The limited deployment reflects systemic bottlenecks in generalizability, data quality, and scalable evaluation methods. Successful adoption requires coordinated development across technical robustness, methodological maturity, and governance integration.