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Multi-modal Summarization in Model-Based Engineering: Automotive Software Development Case Study

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arxiv 2503.04506 v1 pith:2ZC5YU62 submitted 2025-03-06 cs.SE cs.AI

classification cs.SEcs.AI
keywords engineeringmultimodalmodel-basedsummarizationdevelopmentinformationmodelsunderstanding
verification ladder T0 review T1 audit T2 compute T3 formal
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Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. However, the application and advantages of multimodal summarization have not received much attention in model-based engineering (MBE), where it has become a cornerstone in the design and development of complex systems, leveraging formal models to improve understanding, validation and automation throughout the engineering lifecycle. UML and EMF diagrams in model-based engineering contain a large amount of multimodal information and intricate relational data. Hence, our study explores the application of multimodal large language models within the domain of model-based engineering to evaluate their capacity for understanding and identifying relationships, features, and functionalities embedded in UML and EMF diagrams. We aim to demonstrate the transformative potential benefits and limitations of multimodal summarization in improving productivity and accuracy in MBE practices. The proposed approach is evaluated within the context of automotive software development, while many promising state-of-art models were taken into account.

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Cited by 3 Pith papers

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

  1. GenAI-based test case generation and execution in SDV platform

    cs.SE 2025-09 reject novelty 4.0 of 10

    An LLM/VLM pipeline generates and executes a single Gherkin/Python HVAC test for a child-presence-detection system, with manual intervention required at every stage and no quantitative evaluation.

  2. GenAI for Automotive Software Development: From Requirements to Wheels

    cs.SE 2025-07 reject novelty 4.0 of 10

    An architecture proposal for using GenAI in automotive development, without empirical validation.

  3. Survey of GenAI for Automotive Software Development: From Requirements to Executable Code

    cs.SE 2025-07 conditional novelty 3.0 of 10

    A review of roughly 60 papers and 9 industry respondents finds GPT-family models dominate automotive code generation while requirements handling lags due to confidentiality constraints.

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