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We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are <jats:italic>Data Cleansing and Preprocessing<\/jats:italic>, <jats:italic>Textual Feature Extraction, Learning, Evaluation, and Tools<\/jats:italic>. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.<\/jats:p>","DOI":"10.1017\/s0890060422000166","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T07:32:51Z","timestamp":1666769571000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":27,"title":["Machine learning in requirements elicitation: a literature review"],"prefix":"10.1017","volume":"36","author":[{"given":"Cheligeer","family":"Cheligeer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guosong","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadia","family":"Bhuiyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6678-271X","authenticated-orcid":false,"given":"Yong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"S0890060422000166_ref117","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"4","author":"Sutskever","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"S0890060422000166_ref76","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-016-0251-9"},{"key":"S0890060422000166_ref90","doi-asserted-by":"publisher","DOI":"10.1109\/RE.2019.00029"},{"key":"S0890060422000166_ref34","unstructured":"Gnanasekaran, RK , Chakraborty, S , Dehlinger, J and Deng, L (2021) Using recurrent neural networks for classification of natural language-based non-functional requirements. 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