From the course: Generative Analysis: The Power of Generative AI for Object-Oriented Software Engineering with UML by Pearson
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Using UML
In this slide I'm going to introduce the concept of literal modeling. Now we can take a precise enough narrative and use it as a prompt to generate Python code, UML class diagrams and UML requirements. Later, we will demonstrate that such a narrative can be used to grade many kinds of UML artifacts, types of code, databases, documentation, and even simulations. Thus, in a generative AI-assisted analysis approach, the UML model loses some of its attraction. As it stands now, we can only get AI-generated artifacts into a UML model manually via a process of transcription. Similarly, once the artifacts are in the UML model, we can only get information out to create prompts to use with generative AI manually via reverse transcription. Now, this is entirely unsatisfactory, and we're sure that UML tool vendors are addressing this even as we speak. Because sufficiently precise narratives can be used as prompts to generate code and UML artifacts, the narrative begins to take center stage as…
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