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Article overview
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Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data | | Savas Yildirim
; Mucahit Cevik
; Devang Parikh
; Ayse Basar
; | | Date: |
2 Jan 2023 | | Abstract: | The analysis of software requirement specifications (SRS) using Natural
Language Processing (NLP) methods has been an important study area in the
software engineering field in recent years. Especially thanks to the advances
brought by deep learning and transfer learning approaches in NLP, SRS data can
be utilized for various learning tasks more easily. In this study, we employ a
three-stage domain-adaptive fine-tuning approach for three prediction tasks
regarding software requirements, which improve the model robustness on a real
distribution shift. The multi-class classification tasks involve predicting the
type, priority and severity of the requirement texts specified by the users. We
compare our results with strong classification baselines such as word embedding
pooling and Sentence BERT, and show that the adaptive fine-tuning leads to
performance improvements across the tasks. We find that an adaptively
fine-tuned model can be specialized to particular data distribution, which is
able to generate accurate results and learns from abundantly available textual
data in software engineering task management systems. | | Source: | arXiv, 2301.00495 | | Services: | Forum | Review | PDF | Favorites | |
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