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These challenges can be addressed using generative artificial intelligence (GenAI). Given that GenAI\u2010based RE has not been systematically analyzed in detail, this review examines the related research, focusing on trends, methodologies, challenges, and future work directions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Although generative pretrained transformer models dominate current applications (67.3% of studies), the research focus remains unevenly distributed across RE phases, with analysis (30.0%) and elicitation (22.1%) receiving the most attention and management (6.8%) remaining underexplored. Three core challenges\u2014reproducibility (66.8%), hallucinations (63.4%), and interpretability (57.1%)\u2014form a tightly interlinked triad affecting trust and consistency, and strong correlations ( co\u2010occurrence) indicate that these challenges must be addressed holistically. Industrial adoption remains nascent, with &gt; 90% of studies corresponding to early\u2010stage development and only 1.3% reaching production\u2010level integration. Evaluation practices show maturity gaps, limited tool\/dataset availability, and fragmented benchmarking approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Despite the transformative potential of GenAI\u2010based RE, several barriers hinder its practical adoption. The strong correlations among core challenges demand specialized architectures targeting interdependencies rather than isolated solutions. The limited real\u2010world 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. A multiphase research roadmap emphasizing evaluation infrastructure strengthening, governance\u2010aware development, and industrial\u2010scale standardization is proposed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/spe.70029","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T01:01:33Z","timestamp":1762304493000},"page":"141-170","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Generative AI for Requirements Engineering: A Systematic Literature Review"],"prefix":"10.1002","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2265-6437","authenticated-orcid":false,"given":"Haowei","family":"Cheng","sequence":"first","affiliation":[{"name":"Waseda University  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jati H.","family":"Husen","sequence":"additional","affiliation":[{"name":"Waseda University  Tokyo Japan"},{"name":"Telkom University  Jawa Barat Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijun","family":"Lu","sequence":"additional","affiliation":[{"name":"Waseda University  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teeradaj","family":"Racharak","sequence":"additional","affiliation":[{"name":"Advanced Institute of So\u2010Go\u2010Chi (Convergence Knowledge) Informatics, Tohoku University  Miyagi Japan"},{"name":"Japan Advanced Institute of Science and Technology (JAIST)  Ishikawa Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobukazu","family":"Yoshioka","sequence":"additional","affiliation":[{"name":"Waseda University  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoyasu","family":"Ubayashi","sequence":"additional","affiliation":[{"name":"Waseda University  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hironori","family":"Washizaki","sequence":"additional","affiliation":[{"name":"Waseda University  Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"e_1_2_16_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0164-1212(94)00092-2"},{"key":"e_1_2_16_3_1","unstructured":"Group S \u201cBenchmarks and Assessments\u2010Virtual Success Ladder Benchmark. 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