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A systematic mapping study on machine learning methodologies for requirements management
- Source :
- IET Software, Vol 17, Iss 4, Pp 405-423 (2023)
- Publication Year :
- 2023
- Publisher :
- Wiley, 2023.
-
Abstract
- Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.
Details
- Language :
- English
- ISSN :
- 17518814 and 17518806
- Volume :
- 17
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- IET Software
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.9e3c580c0a3f4b589fda342c347eb097
- Document Type :
- article
- Full Text :
- https://doi.org/10.1049/sfw2.12082