Pith. sign in

REVIEW 4 cited by

From Image to UML: First Results of Image Based UML Diagram Generation Using LLMs

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.11376 v2 pith:72CBT727 submitted 2024-04-17 cs.SE

classification cs.SE
keywords modelsimagesactualdiagramsdifferentdrawingsengineeringfirst
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In software engineering processes, systems are first specified using a modeling language such as UML. These initial designs are often collaboratively created, many times in meetings where different domain experts use whiteboards, paper or other types of quick supports to create drawings and blueprints that then will need to be formalized. These proper, machine-readable, models are key to ensure models can be part of automated processes (e.g. input of a low-code generation pipeline, a model-based testing system, ...). But going from hand-drawn diagrams to actual models is a time-consuming process that sometimes ends up with such drawings just added as informal images to the software documentation, reducing their value a lot. To avoid this tedious task, we explore the usage of Large Language Models (LLM) to generate the formal representation of (UML) models from a given drawing. More specifically, we have evaluated the capabilities of different LLMs to convert images of UML class diagrams into the actual models represented in the images. While the results are good enough to use such an approach as part of a model-driven engineering pipeline we also highlight some of their current limitations and the need to keep the human in the loop to overcome those limitations.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Prior Bias in Vision Language Models on UML Diagram Interpretation

    cs.CV 2026-07 conditional novelty 6.5 of 10

    Reversing only the UML relation arrow while keeping class names and layout fixed cuts open-source VLM relation accuracy by about 33%, revealing prior-over-vision bias.

  2. A Formalism-Aware Reward Loop for Handwritten UML-to-PlantUML Generation

    cs.SE 2026-07 conditional novelty 6.0 of 10

    Using XMI and control-flow-graph comparison as reinforcement-learning rewards makes a 4B open vision-language model competitive with larger proprietary models on handwritten UML-to-PlantUML, but the reward stage's ben...

  3. Automated Feedback on Student-Generated UML and ER Diagrams Using Large Language Models

    cs.HC 2025-07 conditional novelty 4.0 of 10

    DUET uses a two-stage LLM pipeline to convert and compare UML and ER diagrams and generate automated student feedback, but its only evaluation so far is six qualitative interviews.

  4. The importance of visual modelling languages in generative software engineering

    cs.SE 2024-11 conditional novelty 4.0 of 10

    Multimodal GPTs can turn UML class, sequence, and hand-drawn activity diagrams into working Python code, and can reverse engineer code back into diagrams, demonstrated across several software engineering tasks.

Pith tools