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Best Practice on Automatic Toon Image Creation from JSON File of Message Sequence Diagram via Natural Language based Requirement Specifications

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dc.contributor.author김현태-
dc.contributor.author공지훈-
dc.contributor.author손현승-
dc.contributor.author김영철-
dc.date.accessioned2024-04-09T06:00:22Z-
dc.date.available2024-04-09T06:00:22Z-
dc.date.issued2024-03-
dc.identifier.issn2288-2847-
dc.identifier.issn2288-2855-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32916-
dc.description.abstractIn AI image generation tools, most general users must use an effective prompt to craft queries or statements to elicit the desired response (image, result) from the AI model. But we are software engineers who focus on software processes. At the process's early stage, we use informal and formal requirement specifications. At this time, we adapt the natural language approach into requirement engineering and toon engineering. Most Generative AI tools do not produce the same image in the same query. The reason is that the same data asset is not used for the same query. To solve this problem, we intend to use informal requirement engineering and linguistics to create a toon. Therefore, we propose a sequence diagram and image generation mechanism by analyzing and applying key objects and attributes as an informal natural language requirement analysis. Identify morpheme and semantic roles by analyzing natural language through linguistic methods. Based on the analysis results, a sequence diagram and an image are generated through the diagram. We expect consistent image generation using the same image element asset through the proposed mechanism.-
dc.format.extent9-
dc.language영어-
dc.language.isoENG-
dc.publisher한국인터넷방송통신학회-
dc.titleBest Practice on Automatic Toon Image Creation from JSON File of Message Sequence Diagram via Natural Language based Requirement Specifications-
dc.title.alternativeBest Practice on Automatic Toon Image Creation from JSON File of Message Sequence Diagram via Natural Language based Requirement Specifications-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.7236/IJASC.2024.13.1.99-
dc.identifier.bibliographicCitationThe International Journal of Advanced Smart Convergence, v.13, no.1, pp 99 - 107-
dc.citation.titleThe International Journal of Advanced Smart Convergence-
dc.citation.volume13-
dc.citation.number1-
dc.citation.startPage99-
dc.citation.endPage107-
dc.identifier.kciidART003067323-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorFillmore’s Case Grammar-
dc.subject.keywordAuthorBerkeley Neural Parser-
dc.subject.keywordAuthorImage Generation-
dc.subject.keywordAuthorCartoon-
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