Leveraging Transformer-based Language Models to Automate Requirements Satisfaction Assessment Article Swipe
Amrit Poudel
,
Jinfeng Lin
,
Jane Cleland‐Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.04463
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.04463
Requirements Satisfaction Assessment (RSA) evaluates whether the set of design elements linked to a single requirement provide sufficient coverage of that requirement -- typically meaning that all concepts in the requirement are addressed by at least one of the design elements. RSA is an important software engineering activity for systems with any form of hierarchical decomposition -- especially safety or mission critical ones. In previous studies, researchers used basic Information Retrieval (IR) models to decompose requirements and design elements into chunks, and then evaluated the extent to which chunks of design elements covered all chunks in the requirement. However, results had low accuracy because many critical concepts that extend across the entirety of the sentence were not well represented when the sentence was parsed into independent chunks. In this paper we leverage recent advances in natural language processing to deliver significantly more accurate results. We propose two major architectures: Satisfaction BERT (Sat-BERT), and Dual-Satisfaction BERT (DSat-BERT), along with their multitask learning variants to improve satisfaction assessments. We perform RSA on five different datasets and compare results from our variants against the chunk-based legacy approach. All BERT-based models significantly outperformed the legacy baseline, and Sat-BERT delivered the best results returning an average improvement of 124.75% in Mean Average Precision.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.04463
- https://arxiv.org/pdf/2312.04463
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389500071
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4389500071Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.04463Digital Object Identifier
- Title
-
Leveraging Transformer-based Language Models to Automate Requirements Satisfaction AssessmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-12-07Full publication date if available
- Authors
-
Amrit Poudel, Jinfeng Lin, Jane Cleland‐HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.04463Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.04463Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2312.04463Direct OA link when available
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Computer science, Leverage (statistics), Sentence, Artificial intelligence, Baseline (sea), Transformer, Set (abstract data type), Parsing, Software engineering, Natural language processing, Machine learning, Programming language, Geology, Oceanography, Physics, Voltage, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.decompose | 74 |
| abstract_inverted_index.delivered | 194 |
| abstract_inverted_index.different | 171 |
| abstract_inverted_index.elements. | 40 |
| abstract_inverted_index.evaluated | 83 |
| abstract_inverted_index.evaluates | 4 |
| abstract_inverted_index.important | 44 |
| abstract_inverted_index.multitask | 159 |
| abstract_inverted_index.returning | 198 |
| abstract_inverted_index.typically | 23 |
| abstract_inverted_index.Assessment | 2 |
| abstract_inverted_index.BERT-based | 185 |
| abstract_inverted_index.Precision. | 207 |
| abstract_inverted_index.especially | 57 |
| abstract_inverted_index.processing | 137 |
| abstract_inverted_index.sufficient | 17 |
| abstract_inverted_index.(Sat-BERT), | 151 |
| abstract_inverted_index.Information | 69 |
| abstract_inverted_index.chunk-based | 181 |
| abstract_inverted_index.engineering | 46 |
| abstract_inverted_index.improvement | 201 |
| abstract_inverted_index.independent | 125 |
| abstract_inverted_index.represented | 118 |
| abstract_inverted_index.requirement | 15, 21, 30 |
| abstract_inverted_index.researchers | 66 |
| abstract_inverted_index.(DSat-BERT), | 155 |
| abstract_inverted_index.Requirements | 0 |
| abstract_inverted_index.Satisfaction | 1, 149 |
| abstract_inverted_index.assessments. | 165 |
| abstract_inverted_index.hierarchical | 54 |
| abstract_inverted_index.outperformed | 188 |
| abstract_inverted_index.requirement. | 97 |
| abstract_inverted_index.requirements | 75 |
| abstract_inverted_index.satisfaction | 164 |
| abstract_inverted_index.decomposition | 55 |
| abstract_inverted_index.significantly | 140, 187 |
| abstract_inverted_index.architectures: | 148 |
| abstract_inverted_index.Dual-Satisfaction | 153 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |