生成式AI正重塑需求工程，但落地仍受限于可信性与系统性瓶颈。
Generative AI for Requirements Engineering: A Systematic Literature Review
- 系统梳理238篇论文，发现生成式模型主导需求分析与获取阶段
- 66.8%研究面临可复现性问题，幻觉与解释性形成互锁难题
- 工业应用不足1.3%，需技术、方法与治理协同突破
需求工程因软件系统复杂度提升而面临挑战，生成式AI或可应对。本文系统回顾2019至2025年发表于主要学术数据库的238篇相关研究，聚焦趋势、方法、挑战与未来方向。结果表明，生成式预训练变换模型占据主导（67.3%），但研究分布不均：分析（30.0%）与获取（22.1%）最受关注，管理仅占6.8%。三大核心挑战——可复现性（66.8%）、幻觉（63.4%）和解释性（57.1%）——存在强关联（共现率35%），需整体解决。工业应用仍处早期，超90%研究为开发阶段，仅1.3%进入生产级集成。评估实践存在成熟度差距，工具与数据集匮乏，基准测试碎片化。尽管生成式AI潜力巨大，技术鲁棒性、方法成熟度与治理整合仍待提升，成功落地需多方协同。
原文摘要 · Abstract (English)
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.
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