Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910127570812928 |
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| author | Matei, Ion Zhenirovskyy, Maksym Sekar, Praveen Kumar Menaka Wong, Hon Yung |
| author_facet | Matei, Ion Zhenirovskyy, Maksym Sekar, Praveen Kumar Menaka Wong, Hon Yung |
| contents | Automatically reconstructing BPMN models from unstructured natural-language descriptions remains challenging due to heterogeneous modeling conventions, multilingual sources, and the lack of reliable ground truth. We present a scalable, multi-stage LLM-driven pipeline that automates both ground-truth construction and model reconstruction. Multilingual BPMN XML files are translated into English, validated using execution-oriented compliance checks in SpiffWorkflow, and iteratively repaired through targeted LLM-guided corrections to produce a consistent ground-truth corpus. From these validated models, process descriptions are generated and used to reconstruct executable BPMN~2.0 XML diagrams without manual curation. We introduce a multi-dimensional similarity framework combining structural metrics, type-distribution alignment, and embedding-based semantic measures. In an empirical study of 750 public BPMN diagrams, the pipeline generated 387 validated ground-truth models and achieved average reconstruction similarity above 0.75, including approximately 50 near-perfect reconstructions differing only in minor naming variations. The results demonstrate that LLMs can generate structurally compliant and semantically meaningful BPMN diagrams at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12105 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach Matei, Ion Zhenirovskyy, Maksym Sekar, Praveen Kumar Menaka Wong, Hon Yung Software Engineering Automatically reconstructing BPMN models from unstructured natural-language descriptions remains challenging due to heterogeneous modeling conventions, multilingual sources, and the lack of reliable ground truth. We present a scalable, multi-stage LLM-driven pipeline that automates both ground-truth construction and model reconstruction. Multilingual BPMN XML files are translated into English, validated using execution-oriented compliance checks in SpiffWorkflow, and iteratively repaired through targeted LLM-guided corrections to produce a consistent ground-truth corpus. From these validated models, process descriptions are generated and used to reconstruct executable BPMN~2.0 XML diagrams without manual curation. We introduce a multi-dimensional similarity framework combining structural metrics, type-distribution alignment, and embedding-based semantic measures. In an empirical study of 750 public BPMN diagrams, the pipeline generated 387 validated ground-truth models and achieved average reconstruction similarity above 0.75, including approximately 50 near-perfect reconstructions differing only in minor naming variations. The results demonstrate that LLMs can generate structurally compliant and semantically meaningful BPMN diagrams at scale. |
| title | Automated BPMN Model Generation from Textual Process Descriptions: A Multi-Stage LLM-Driven Approach |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.12105 |