Ambiguity Detection and Elimination in Automated Executable Process Modeling
Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However, ambiguous or underspecified text can yield structurally valid models with different simulated behavior. Our goal is not to prove that one generated BPMN model is semantically correct, but to detect when a natural-language specification fails to support a stable executable interpretation under repeated generation and simulation. We present a diagnosis-driven framework that detects behavioral inconsistency from the empirical distribution of key performance indicators (KPIs), localizes divergence to gateway logic ...
| Authors | Ion Matei, Praveen Kumar Menaka Sekar, Maksym Zhenirovskyy et al. |
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| Primary category | cs.SE |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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