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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李允中 | zh_TW |
| dc.contributor.advisor | Jonathan Lee | en |
| dc.contributor.author | 朱紹瑜 | zh_TW |
| dc.contributor.author | Shao-Yu Chu | en |
| dc.date.accessioned | 2024-08-16T17:11:46Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-12 | - |
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On ordered weighted averaging aggregation operators in multicriteria deci- sionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1):183–190, 1988. [28] D. Zan, B. Chen, D. Yang, Z. Lin, M. Kim, B. Guan, Y. Wang, W. Chen, and J.-G. Lou. CERT: Continual pre-training on sketches for library-oriented code generation. In The 2022 International Joint Conference on Artificial Intelligence, 2022. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94630 | - |
| dc.description.abstract | 近年來，Event Storming 方法在軟體設計領域逐漸受到關注。然而，Event Storming 仰賴領域專家長時間參與，實施條件嚴苛。為了解決這個問題，我們提出了一種自動化的 Event Storming 流程，可用於從軟體需求中推導出 bounded contexts。該方法先從使用案例規格中提取 domain events，接著運用自然語言處理技術及外部知識庫的領域專業知識，找出領域模型中重要的 entities 及 attributes，進而依循 Domain-Driven Design 的概念將其聚合為 aggregates 和 bounded contexts。該方法有效減少了對領域專家參與的依賴，提高軟體設計的效率。 | zh_TW |
| dc.description.abstract | Event Storming has gained attention as an innovative method for software design. However, conducting Event Storming is challenging due to the need for extensive involvement from domain experts and stakeholders. In response to this challenge, we propose an automated Event Storming process aimed at deriving bounded contexts from software requirements. This process begins by extracting domain events from use case specifications. It then identifies the entities and attributes in the domain model by leveraging Natural Language Processing techniques and integrating domain knowledge sourced from external knowledge bases. These entities are subsequently aggregated into aggregates and bounded contexts in accordance with Domain-Driven Design principles. This method effectively reduces the dependency on domain experts' involvement and enhances the efficiency of software design. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T17:11:46Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T17:11:46Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures viii List of Tables x Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Background Work 4 2.1.1 Domain-Driven Design 4 2.1.2 Event Storming 5 2.1.3 EARS Requirements 6 2.2 Related Work 6 2.2.1 Leveraging Domain Knowledge in Domain Modeling 6 2.2.2 Mapping Natural Language to Programming Language 7 2.2.2.1 CodeBERT 7 2.2.2.2 CodeGPT 7 Chapter 3 The Process of Deriving Bounded Contexts 8 3.1 Collect Domain Events 10 3.2 Extract Entities and Attributes 11 3.2.1 NLP-Based Approach 11 3.2.1.1 Syntactic Dependency Parsing 12 3.2.1.2 Semantic Analysis 12 3.2.1.3 Rules 13 3.2.2 Knowledge Graph-Based Approach 14 3.2.2.1 Group Use Cases Based on Use Case Relationships 16 3.2.2.2 Identify the Domain Objects and Attributes Within Each Use Case Group 19 3.2.2.3 Determine Which Domain Object Each Attribute Is Associated With 19 3.2.3 Combining the Extraction Results 20 3.3 Identify Aggregates 20 3.4 Cluster Aggregates into Bounded Contexts 21 3.4.1 Calculate Distances Between Aggregates 22 3.4.1.1 Similarity in WordNet 23 3.4.1.2 Relatedness in ConceptNet 24 3.4.1.3 Aggregating the Metrics 24 3.4.2 Clustering Algorithm 25 3.4.2.1 Density-Based Clustering 25 3.4.2.2 Find the Optimal Number of Clusters 26 3.4.2.3 Hierarchical Clustering 27 Chapter 4 Case Study: Unified University Inventory System 29 4.1 Input 29 4.2 Domain Events 32 4.3 Entities and Attributes 33 4.4 Aggregates 35 4.5 Bounded Contexts 35 Chapter 5 Conclusion 39 Chapter 6 Future Work 40 References 41 Appendix A — WordNet Lexical Semantic Categories 45 A.1 Action Verbs 45 A.2 Change-Inducing Verbs 46 Appendix B — Relations in ConceptNet 47 | - |
| dc.language.iso | en | - |
| dc.subject | 領域驅動設計 | zh_TW |
| dc.subject | 事件腦力激盪 | zh_TW |
| dc.subject | 知識庫 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 軟體設計 | zh_TW |
| dc.subject | Software Design | en |
| dc.subject | Natural Language Processing | en |
| dc.subject | Knowledge Base | en |
| dc.subject | Domain-Driven Design | en |
| dc.subject | Event Storming | en |
| dc.title | 自動化事件腦力激盪：從 EARS 需求推導出受限制的前後文內容 | zh_TW |
| dc.title | Automate Event Storming Process to Derive Bounded Contexts from EARS Requirements | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 鄭永斌;蘇木春;王小璠;薛念林 | zh_TW |
| dc.contributor.oralexamcommittee | Yung-Pin Cheng;Mu-Chun Su;Hsiao-Fan Wang;Nien-Lin Hsueh | en |
| dc.subject.keyword | 事件腦力激盪,領域驅動設計,軟體設計,自然語言處理,知識庫, | zh_TW |
| dc.subject.keyword | Event Storming,Domain-Driven Design,Software Design,Natural Language Processing,Knowledge Base, | en |
| dc.relation.page | 50 | - |
| dc.identifier.doi | 10.6342/NTU202404153 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-13 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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