DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language
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| Publicat a: | CAAI Transactions on Intelligence Technology vol. 9, no. 1 (Feb 1, 2024), p. 250 |
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| Autor principal: | |
| Altres autors: | , , , , |
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John Wiley & Sons, Inc.
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| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3192195979 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2468-2322 | ||
| 024 | 7 | |a 10.1049/cit2.12207 |2 doi | |
| 035 | |a 3192195979 | ||
| 045 | 0 | |b d20240201 | |
| 100 | 1 | |a Yang, Yilong |u School of Software, Beihang University, Beijing, China | |
| 245 | 1 | |a DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language | |
| 260 | |b John Wiley & Sons, Inc. |c Feb 1, 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry. | |
| 653 | |a Language | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Syntax | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Bar codes | ||
| 653 | |a Ablation | ||
| 653 | |a Project management | ||
| 653 | |a Formal specifications | ||
| 653 | |a Knowledge management | ||
| 653 | |a Program verification (computers) | ||
| 653 | |a Machine learning | ||
| 653 | |a Queries | ||
| 653 | |a Constraints | ||
| 653 | |a Software engineering | ||
| 653 | |a Software | ||
| 653 | |a Semantics | ||
| 653 | |a Natural language | ||
| 700 | 1 | |a Liu, Yibo |u School of Software, Beihang University, Beijing, China | |
| 700 | 1 | |a Bao, Tianshu |u College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou, China | |
| 700 | 1 | |a Wang, Weiru |u Faculty of Information Technology, Beijing University of Technology, Beijing, China | |
| 700 | 1 | |a Niu, Nan |u Department of Electrical Engineering and Computer Sciences, University of Cincinnati, Cincinnati, Ohio, USA | |
| 700 | 1 | |a Yin, Yongfeng |u School of Software, Beihang University, Beijing, China | |
| 773 | 0 | |t CAAI Transactions on Intelligence Technology |g vol. 9, no. 1 (Feb 1, 2024), p. 250 | |
| 786 | 0 | |d ProQuest |t Computer Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3192195979/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3192195979/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3192195979/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |