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NoRBERT: Transfer Learning for Requirements Classification

Hey, Tobias ORCID iD icon; Keim, Jan; Koziolek, Anne ORCID iD icon; Tichy, Walter F. ORCID iD icon

Abstract (englisch):

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results $\text{F}_1$-scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying non-functional requirements subclasses. The most frequent classes are classified with an average $\text{F}_1$-score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of 15 percentage points in average $\text{F}_1$-score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. ... mehr

Postprint §
DOI: 10.5445/IR/1000124657
Veröffentlicht am 01.09.2021
DOI: 10.1109/RE48521.2020.00028
Zitationen: 48
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 31.08.2020
Sprache Englisch
Identifikator ISBN: 978-1-72817-438-9
KITopen-ID: 1000124657
Erschienen in 2020 IEEE 28th International Requirements Engineering Conference (RE)
Veranstaltung 28th IEEE International Requirements Engineering Conference (RE 2020), Zürich, Schweiz, 31.08.2020 – 04.09.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 169–179
Schlagwörter Requirements Classification, Requirements Engineering, Machine Learning, Transfer Learning, Language Model, BERT
Nachgewiesen in Dimensions
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