Iterative Requirements-Driven Business Process Modeling and Verification with Large Language Models

Contemporary business process modeling lacks a systematic framework for converting unstructured requirements into structured models. Traditional manual approaches fail to support integrated lifecycle management from requirements elicitation to iterative model refinement. The gap severely limits the efficiency and accuracy of the alignment between requirements and business process modeling and often leads to costly rework and implementation errors in complex software projects. Therefore, this paper aims to establish a coherent modeling framework from requirements extraction to business process model verification. The framework maintains the traceability and consistency of the unstructured requirements through three tasks: (1) automatic generation of a structured requirements model from textual input to a set of designed prompts of hyperparameter-optimized large language models (LLMs); (2) establishment of a modeling routine to handle the iterative requirements via two sets of formalized mapping rules, a merging algorithm, and a toolkit; (3) detection of the obtained CBPMN model by a static flow error verification algorithm and reachability verification using CPN tools 4.0. A total of 15 sets of comparative experiments with three state-of-the-art automated modeling approaches demonstrate the superiority of our method in generating higher-quality requirements models, while an additional case study with two-step verification proves its validity.