Algorithmic and meta-algorithmic machine learning natural language processing approaches for stakeholder requirements classification. Issue 1 (17th May 2023)

• Record Type: Journal Article
• Authors: Jr., Arturo N. Villanueva; Simske, Steven J.
• Is Part Of: International journal of computational systems engineering. Volume 7:Issue 1(2022)
• Page Start: 41
• Page End: 56
• Publication Date: 2023-05-17
• Subjects: natural language processing -- NLP -- classification -- meta-algorithms -- machine learning -- statements of works -- SOWs -- requests for proposals -- RFPs -- document classification; Computer science -- Periodicals; Electronic data processing -- Periodicals; System analysis -- Periodicals; 003.3
• Languages: English
• ISSNs: 2046-3391

Abstract

Requirements engineering begins with discovery at the outset of project acquisition. Documents typically used during this phase include statements of works (SOWs) and requests for proposals (RFPs). One of the first challenges of a systems engineer is to carefully classify requirements into appropriate bins for further processing. This manual process, fundamental to understanding stakeholder needs and architecting and designing the system(s) of interest, is often tedious, particularly for large projects that start out with thousands of requirements embedded in these documents, making the task ripe for automation. For this research, we investigate multiple combinations of algorithms and meta-algorithms to glean insight as to how well they perform on this aspect of one of the more mundane aspects of requirements engineering. We obtain, by running various training corpora representing multiple industries through our pipelines of (meta-)algorithms, some understanding of what works best and what and how they could be improved.