Document Type

Article

Publication Date

2022

DOI

10.1017/S0890060422000166

Publication Title

AI EDAM

Volume

36

Pages

e32 (1-23)

Abstract

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.

Rights

© The Authors 2022.

This is an open access article, distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

ORCID

0000-0003-2155-6107 (Huang)

Original Publication Citation

Cheligeer, C., Huang, J. W., Wu, G. S., Bhuiyan, N., Xu, Y., & Zeng, Y. (2022). Machine learning in requirements elicitation: A literature review. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 36, 1-23, Article e32. https://doi.org/10.1017/S0890060422000166

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