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Cross-applicability of ML classification methods intended for (non-)functional requirements

Nguyen, Thuy (2021) Cross-applicability of ML classification methods intended for (non-)functional requirements.

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Abstract:Machine Learning (ML) has been applied to a wide variety of feeds and achieved significantly promising results. Its power arises in the ability to learn from data and make decisions based on its learning. Recognizing the impact of this cutting edge technology and how it can benefit Requirements Engineering (RE), researchers have tried applying different ML approaches onto RE tasks. The literature review “The Landscape of Machine Learning in Requirements Engineering” shows that in recent years, a plethora of ML techniques have been proposed to solve the problem of classifying requirements, targeted specifically to functional or nonfunctional requirements. In this study, we experiment the cross-applicability of these ML methods, that are intended for either functional or nonfunctional requirements, when being used to classify the other. The ML techniques found in our literature review will be re-evaluated on a common dataset of (non-)functional requirements, and then be used to classify the other to compare their effectiveness. With this study, we hope to put a conclusion to our hypothesis that although not designed to, ML methods intended for classifying functional requirements can be effectively used for non-functional requirements, and vice versa.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/88236
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