NoRBERT: Transfer Learning for Requirements Classification
Authors: Hey, Tobias; Keim, Jan; Koziolek, Anne; Tichy, Walter F.
Conference: RE'20: 2020 IEEE 28th International Requirements Engineering Conference, September 2nd, 2020
Paper
Abstract: 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 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 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 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. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F 1 -score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results.
@inproceedings{hey_norbert_2020,
title = {{{NoRBERT}}: {{Transfer Learning}} for {{Requirements Classification}}},
shorttitle = {{{NoRBERT}}},
booktitle = {2020 {{IEEE}} 28th {{International Requirements Engineering Conference}} ({{RE}})},
author = {Hey, T. and Keim, J. and Koziolek, A. and Tichy, W. F.},
year = {2020},
month = aug,
pages = {169--179},
issn = {2332-6441},
doi = {10.1109/RE48521.2020.00028}
}