원문정보
초록
영어
In 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.
목차
1. Introduction
2. Related Study
2.1 Berkeley Neural Parser
2.2 Redefined Fillmore Case Grammar
2.3 Study on Code Extraction from Requirements
3. Image Generation Mechanism
3.1 Sentences Preprocessing
3.2 Key Attributes Extraction Using the Berkely Neural Parser
3.3 Role Extracting on the Sentence Structure
3.4 Mapping Sequence Diagram Key Attributes with Case Grammar
3.5 Mapping Sequence Diagrams with Image Properties
3.6 Image Generation
4. Conclusion
Acknowledgement
References
