Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data Article Swipe
Savaş Yıldırım
,
Mücahit Çevik
,
Devang Parikh
,
Ayşe Bener
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.00495
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.00495
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.
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- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.00495
- https://arxiv.org/pdf/2301.00495
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4313484221Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2301.00495Digital Object Identifier
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Adaptive Fine-tuning for Multiclass Classification over Software Requirement DataWork title
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preprintOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-01-02Full publication date if available
- Authors
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Savaş Yıldırım, Mücahit Çevik, Devang Parikh, Ayşe BenerList of authors in order
- Landing page
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https://arxiv.org/abs/2301.00495Publisher landing page
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https://arxiv.org/pdf/2301.00495Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2301.00495Direct OA link when available
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Computer science, Artificial intelligence, Transfer of learning, Software, Machine learning, Sentence, Pooling, Field (mathematics), Robustness (evolution), Embedding, Deep learning, Natural language processing, Biochemistry, Chemistry, Programming language, Gene, Pure mathematics, MathematicsTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.engineering | 22, 158 |
| abstract_inverted_index.fine-tuning | 61, 120 |
| abstract_inverted_index.multi-class | 81 |
| abstract_inverted_index.performance | 123 |
| abstract_inverted_index.requirement | 4, 93 |
| abstract_inverted_index.specialized | 137 |
| abstract_inverted_index.three-stage | 59 |
| abstract_inverted_index.distribution | 78 |
| abstract_inverted_index.improvements | 124 |
| abstract_inverted_index.distribution, | 141 |
| abstract_inverted_index.requirements, | 69 |
| abstract_inverted_index.classification | 82, 105 |
| abstract_inverted_index.specifications | 5 |
| abstract_inverted_index.domain-adaptive | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7599999904632568 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |