Stereotypes as Design Patterns for Serious Games to Enhance Software Comprehension

Gokmen, Rene; Heidrich, David; Schreiber, Andreas; Bichlmeier, Christoph

doi:10.1109/cog52621.2021.9619060

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Such dynamic visualizations could even adapt in real time, which would be invaluable for understanding and debugging complex software behavior as it is being built. Additionally, generative AI could also generate content for visualization systems or learning and support environments based on gamification or serious games [21].…”

Section: Discussionmentioning

confidence: 99%

Visualizing Source Code as Comics Using Generative AI

Heidrich,

Schreiber

2023

2023 IEEE Working Conference on Software Visualization (VISSOFT)

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The architecture and inner structure of software is often only implicitly available in the form of its source code and thus not tangible and intuitively easy to understand for nonprogrammers and laymen. Our goal is to create visualizations as automatically as possible, with which such people can nevertheless understand the software or parts of the software and get a feel for the structure of the software and how its methods work. Especially for newcomers to software projects, for management or even for students and pupils, it can be helpful to get a nontechnical insight into the software.We use the concept of visualizing information as comics to present aspects of the software as strikingly as possible, as comics are an effective way to present complex systems and interrelationships for certain target groups. For this purpose, we present a method to generate comics from source code. Our semi-automated process is based on generating a prompt for an LLM from source code, which in turn generates a prompt for a comic image generation using the text-to-image model Stable Diffusion. We show that generative AI methods can be used to rapidly generate human-compatible artistic representations from source code. However, further research is needed to validate the understandability of the results.

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Section: Discussionmentioning

confidence: 99%