Using OpenAI's GPT-4 Vision to Convert Flowcharts into Models | Test Modeller

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The recent announcement of GPT-4 with vision capabilities by OpenAI stands as a groundbreaking development in multi-modal language models. GPT-4 with Vision, sometimes referred to as GPT-4V or gpt-4-vision-preview in the API, allows the model to take in images and answer questions about them.

In this video, we are leveraging GPT Vision to un-silo valuable information, converting “static” artifacts into “living documentation”. For instance, we can convert computer-generated flowcharts, wireframes and whiteboard images directly into Modeller’s flowcharts. These “active” integrated flows are then used to drive collaborative requirements engineering, accurate development and automated test generation.

For flowcharts, this process begins by creating visual representations of the application's processes, which are then fed into the system. The co-pilot intelligently analyses these visuals, interpreting and converting them into detailed, editable flowcharts within our modelling tool.

This approach is particularly effective for flowcharting applications that are visually rich, but lack the necessary export functionalities. Bypassing the need for manual data entry or complex integration solutions, we can swiftly convert static images into dynamic, interactive models that accurately reflect an application's intended behaviour. This process yields accurate results because the input images (flowcharts) are themselves computer-generated, and therefore lend themselves well to automated, AI-augmented analysis.