A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. 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 Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. 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.

中文翻译：

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需求获取中的机器学习：文献综述

需求获取的一个增长趋势是使用机器学习 (ML) 技术来自动化繁琐的需求处理过程。这篇文献综述总结并分析了将 ML 和自然语言处理 (NLP) 纳入需求获取的研究。我们回答以下研究问题：(1) ML 支持哪些需求启发活动？(2) 哪些数据源用于构建基于 ML 的需求解决方案？(3) 构建基于ML的需求获取采用了哪些技术、算法和工具？(4) 如何构建基于ML的需求获取方法？(5) 支持基于 ML 的需求获取方法的可用工具有哪些？来自这些研究问题的关键词导致最初从 7 个科学搜索引擎检索到 975 条记录。最后，86 篇文章被选中纳入审查。作为主要研究发现，我们确定了 15 个基于 ML 的需求获取任务，并将它们分为四类。本文献综述确定并分类了用于构建数据驱动模型的 12 种不同数据源。此外，我们将构建基于 ML 的需求获取方法的技术分为五个部分，分别是数据清理和预处理,文本特征提取、学习、评估和工具. 更具体地说，确定了 3 类预处理方法、3 种不同的特征提取策略、12 种不同的学习方法、2 种不同的评估策略以及各种现成的公开可用工具。此外，我们讨论了当前研究的局限性，并提出了未来研究的八个潜在方向。