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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李允中 | |
| dc.contributor.author | Cheng-Chun Yuan | en |
| dc.contributor.author | 袁晟峻 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:42:50Z | - |
| dc.date.available | 2024-08-13 | |
| dc.date.copyright | 2019-08-13 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-07 | |
| dc.identifier.citation | [1] Malware wiki. https://malware.wikia.org/wiki/Main_Page.
[2] Military wiki. https://military.wikia.org/wiki/Main_Page. [3] Plantuml. http://plantuml.com/en/. [4] Psychology wiki. https://psychology.wikia.org/wiki/Psychology_Wiki. [5] Religion wiki. https://religion.wikia.org/wiki/Portal. [6] Scrapy. https://scrapy.org/. [7] Wikia (fandom). https://www.fandom.com/. [8] Wikipedia. https://en.wikipedia.org/wiki/Main_Page. [9] C. Arora, M. Sabetzadeh, L. Briand, and F. Zimmer. Extracting domain models from natural-language requirements: Approach and industrial evaluation. In Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, MODELS ’16, pages 250–260, New York, NY, USA, 2016. ACM. [10] D. Chen and C. Manning. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 740–750, Doha, Qatar, Oct. 2014. Association for Computational Linguistics. [11] S. Cucerzan. Large-scale named entity disambiguation based on Wikipedia data. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pages 708–716, Prague, Czech Republic, June 2007. Association for Computational Linguistics. [12] J. Devlin, M. Chang, K. Lee, and K. Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. CoRR, abs/1810.04805, 2018. [13] O.-E. Ganea, M. Ganea, A. Lucchi, C. Eickhoff, and T. Hofmann. Probabilistic bag-of-hyperlinks model for entity linking. In Proceedings of the 25th International Conference on World Wide Web, WWW ’16, pages 927–938, Republic and Canton of Geneva, Switzerland, 2016. International World Wide Web Conferences Steering Committee. [14] S. Gulia and T. Choudhury. An efficient automated design to generate uml diagram from natural language specifications. In 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pages 641–648, Jan 2016. [15] C. A. Gunter, E. L. Gunter, M. Jackson, and P. Zave. A reference model for requirements and specifications. IEEE Software, 17(3):37–43, May 2000. [16] Z. Guo and D. Barbosa. Robust entity linking via random walks. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM ’14, pages 499–508, New York, NY, USA, 2014. ACM. [17] M. Ibrahim and R. Ahmad. Class diagram extraction from textual requirements using natural language processing (nlp) techniques. In 2010 Second International Conference on Computer Research and Development, pages 200–204, May 2010. [18] P.-Y. Liu. Deciphering news with event embedding. Master’s thesis, National Taiwan University, 2018. [19] Liwu Li. Translating use cases to sequence diagrams. In Proceedings ASE 2000. Fifteenth IEEE International Conference on Automated Software Engineering, pages 293–296, Sep. 2000. [20] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky. The Stanford CoreNLP natural language processing toolkit. In Association for Computational Linguistics (ACL) System Demonstrations, pages 55–60, 2014. [21] M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini. Building a large annotated corpus of english: The penn treebank. Comput. Linguist., 19(2):313–330, June 1993. [22] D. Milne and I. H. Witten. Learning to link with wikipedia. In Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08, pages 509–518, New York, NY, USA, 2008. ACM. [23] A. Mitchell, S. Strassel, S. Huang, and R. Zakhary. Ace 2004 multilingual training corpus. Philadelphia: Linguistic Data Consortium, 2005. [24] J. Nivre, M.-C. de Marneffe, F. Ginter, Y. Goldberg, J. Hajic, C. D. Manning, R. Mc- Donald, S. Petrov, S. Pyysalo, N. Silveira, R. Tsarfaty, and D. Zeman. Universal dependencies v1: A multilingual treebank collection. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 1659–1666, Portoroˇz, Slovenia, May 2016. European Language Resources Association (ELRA). [25] J. Nivre and M. Scholz. Deterministic dependency parsing of english text. In Proceedings of the 20th International Conference on Computational Linguistics, COLING ’04, Stroudsburg, PA, USA, 2004. Association for Computational Linguistics. [26] J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, 2014. [27] I.-Y. Song, K. Yano, J. Trujillo, and S. Lujan-Mora. A taxonomic class modeling methodology for object-oriented analysis. In 2005 Information Modeling Methods and Methodologies, pages 216–240, 2005. [28] K. Toutanova and C. D. Manning. Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In Proceedings of the 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora: Held in Conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13, EMNLP ’00, pages 63–70, Stroudsburg, PA, USA, 2000. Association for Computational Linguistics. [29] Z. Yang, Z. Dai, Y. Yang, J. G. Carbonell, R. Salakhutdinov, and Q. V. Le. Xlnet: Generalized autoregressive pretraining for language understanding. CoRR, abs/1906.08237, 2019. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74562 | - |
| dc.description.abstract | 在軟體工程的領域裡面，軟體設計一直是個很重要的研究主題，在之前已有許多研究提出各種自動或半自動的方式從需求生成軟體設計，
但沒有人能夠從無限制的自然語言寫成的需求中，自動地生成品質尚可的軟體設計。 為此我們提出了一個基於神經網路的方法，這個方法能夠學習需求中物件之間的依賴關係，減輕自然語言的複雜度所帶來的問題，最後自動生成設計圖。 此外，我們也提出一個實體鏈接的方法去驗證自動生成的設計圖。 在本研究中，我們專注在生成類別圖與循序圖。 我們的神經網路模型在類別圖的依賴關係分析的部分，達到了77%的結構準確率以及70%的依賴關係準確率。 而實體鏈接驗證在實驗中達到了80%的準確率。 | zh_TW |
| dc.description.abstract | Software design from requirements has long been an important research topic in software engineering.
Previous research has proposed many automatic or semi-automatic methods to generate software design from requirements, but none of them can automatically parse requirements in unrestricted natural language with an acceptable result. We propose a neural network-based approach to learning the dependencies between objects in requirements, alleviating the problem caused by the complexity of natural language, and generating the UML diagram automatically. An entity linking approach is also proposed to verify the generated diagrams. In this work, we focus on the generation of class diagrams and sequence diagrams. Our neural network model reaches 77% structural accuracy and 70% label accuracy on class diagram dependency parsing. The entity linking verification reaches 80% precision in the experiment. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:42:50Z (GMT). No. of bitstreams: 1 ntu-108-R06922152-1.pdf: 3406367 bytes, checksum: d8fd54f7914aeb4c862a92b310405d92 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 ii
摘要 iii Abstracts iv List of Figures viii List of Tables xi Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Pretraining Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 BERT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 XLNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Word Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 GloVe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 NLP Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.1 Stanford CoreNLP . . . . . . . . . . . . . . . . . . . . . . . . 6 2.4 Entity Linking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Algorithms used for Entity Linking . . . . . . . . . . . . . . . . . . . 8 2.5.1 Wiki Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.5.2 Probabilistic Bag-Of-Hyperlinks . . . . . . . . . . . . . . . . . 8 2.5.3 Random Walk Algorithm . . . . . . . . . . . . . . . . . . . . . 9 2.5.4 Rerank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.6 UML Diagram Generation . . . . . . . . . . . . . . . . . . . . . . . . 10 2.6.1 Class Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.6.2 Sequence Diagram . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 3 From Requirements to Design 13 3.1 Dependency Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Class Diagram Dependency Parsing Model . . . . . . . . . . . . . . . 19 3.2.1 Transition-Based Dependency Parsing . . . . . . . . . . . . . 19 3.2.2 Neural Network Architecture . . . . . . . . . . . . . . . . . . . 23 3.3 Class Diagram Constructor . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.2 PlantUML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Verification through Entity Linking . . . . . . . . . . . . . . . . . . . 27 3.4.1 Generic Entity Linking . . . . . . . . . . . . . . . . . . . . . . 28 3.4.2 Domain Selection . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.4.3 Domain Entity Linking . . . . . . . . . . . . . . . . . . . . . . 35 3.5 From Class Diagrams to Graphs to Subgraphs . . . . . . . . . . . . . 35 Chapter 4 Experiment 40 4.1 Class Diagram Dependency Parsing . . . . . . . . . . . . . . . . . . . 40 4.1.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2 Entity Linking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Chapter 5 Conclusion 44 Bibliography 45 A Iterative DFS Example 49 B A Peer Review System Example from the Requirement to Diagrams 55 | |
| dc.language.iso | en | |
| dc.subject | 依賴關係分析 | zh_TW |
| dc.subject | 軟體設計 | zh_TW |
| dc.subject | 需求工程 | zh_TW |
| dc.subject | 實體鏈接 | zh_TW |
| dc.subject | 軟體工程 | zh_TW |
| dc.subject | dependency parsing | en |
| dc.subject | software engineering | en |
| dc.subject | software design | en |
| dc.subject | requirement engineering | en |
| dc.subject | entity linking | en |
| dc.title | 以實體鏈接方法從需求生成軟體設計 | zh_TW |
| dc.title | Generating Software Design from Requirement: An Entity-Linking Approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蘇木春,郭忠義,劉建宏,李文廷 | |
| dc.subject.keyword | 軟體工程,軟體設計,需求工程,依賴關係分析,實體鏈接, | zh_TW |
| dc.subject.keyword | software engineering,software design,requirement engineering,dependency parsing,entity linking, | en |
| dc.relation.page | 64 | |
| dc.identifier.doi | 10.6342/NTU201902721 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-07 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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