bpmn modeling

Existing BPMN+DMN process models lack semantic-level automated verification; mainstream tools support only syntactic validation, while behavioral errors require manual execution and debugging, and model transformations remain opaque. Method: We propose the first end-to-end automated verification framework that (i) formally translates BPMN+DMN models into semantics-preserving Java programs; (ii) synthesizes interactive test plans via symbolic execution and input-domain disambiguation; and (iii) provides structured coverage analysis at both node and edge levels. Results: Evaluated on established benchmark processes from the literature, our approach significantly improves semantic defect detection, achieves an average test coverage of 89.3%, and accelerates verification by over 20× compared to manual methods.

This paper addresses the incompleteness and insufficient semantic coverage in existing translations from BPMN 2.0 to the AI planning language PDDL. We propose the first semantics-complete mapping method supporting all core BPMN elements—tasks, events, sequence flows, and parallel/inclusive gateways. Our approach establishes an end-to-end pipeline comprising BPMN parsing, semantics-preserving transformation, and non-deterministic PDDL model generation, enabling fully automated derivation of executable PDDL models from BPMN process diagrams. We further integrate a non-deterministic planner to perform execution-path reasoning and generate valid behavioral trajectories. Experimental evaluation confirms that all generated trajectories strictly adhere to BPMN semantics. The method significantly broadens the applicability of AI planning to business process automation, analysis, and optimization. By providing a scalable, formal foundation, it advances process intelligence for modeling, verification, and decision support in enterprise workflows.

This work addresses the fragility of XML parsing and low editing success rates in natural-language-driven BPMN modeling. We propose an LLM-based process modeling approach leveraging a domain-specific JSON representation. Our core contributions are: (1) a lightweight, semantically explicit BPMN JSON Schema that replaces XML as the LLM’s input/output interface; (2) a quality evaluation framework combining graph edit distance (GED) and normalized GED to quantify structural fidelity, alongside a binary success metric to assess editing reliability. Experiments show that our method achieves process generation similarity comparable to XML-based baselines, while significantly improving editing success rate, inference speed, and robustness. The implementation is open-sourced, establishing a more efficient and resilient paradigm for LLM-powered business process modeling.

Data-aware business process models (e.g., BPMN) lack formal execution semantics and automated verification support, hindering rigorous analysis and trustworthiness. Method: This paper introduces BPMN-ProX—the first executable BPMN testing framework integrating formal verification with low-code development. It is grounded in a rigorous formal semantics for data-aware BPMN, incorporates state-of-the-art model checking for automated state-space exploration, and provides a graphical, low-code interactive interface. Contribution/Results: BPMN-ProX pioneers the embedding of formal verification capabilities directly into low-code process platforms, bridging the gap between theoretical modeling and industrial practice. Experimental evaluation demonstrates significant improvements in verification efficiency and accuracy, enables non-technical stakeholders to participate in collaborative modeling, and validates high practicality, scalability, and system trustworthiness in real-world deployments.

To address challenges in commercial management system development—including poor alignment between process models and execution platforms, low model reusability, and suboptimal development efficiency—this paper proposes a metamodel-based Model-Driven Development (MDD) approach. We design an evolvable and extensible business process metamodel framework and introduce a staged model transformation mechanism supporting QVT/ATL, enabling automated adaptation of extended BPMN models to diverse execution platforms. Crucially, we deeply integrate MDD into BPM system construction, establishing business models as the authoritative source governing system behavior. Experimental evaluation demonstrates significant improvements in development productivity and model consistency, robust cross-platform model reuse, and validates the metamodel’s effectiveness and flexibility in extended application scenarios such as resource management and customer relationship management.

This work addresses the challenge of behavioral inconsistency in automatically generated BPMN models due to semantic ambiguity in natural language process descriptions. It proposes the first closed-loop diagnosis and repair framework that operates without requiring ground-truth BPMN annotations. By analyzing the distribution of key performance indicators (KPIs) across multiple model generations, the approach identifies behavioral variations and employs model-based diagnostic techniques to pinpoint gateway logic discrepancies. These discrepancies are traced back to specific source text fragments, which are then refined through an evidence-driven textual revision process. Evaluated on clinical guidelines for diabetic kidney disease management, the method significantly reduces behavioral variability in regenerated models and enhances the semantic stability of executable process models, establishing an end-to-end mapping from behavioral inconsistency to targeted textual correction.

This study addresses the lack of a systematic synthesis and cross-directional integration of formal grammars in business process management (BPM). Through a systematic literature review of 34 core studies, it identifies and integrates seven distinct application areas of formal grammars in BPM for the first time, revealing their largely isolated development. Leveraging theoretical foundations such as the Bunge-Wand-Weber ontology, process algebras, graph grammars, attribute grammars, and grammar inference, the work constructs a comprehensive taxonomy that clarifies the role of formal grammars across the entire BPM lifecycle—including process design, modeling, execution, verification, and mining. Furthermore, it articulates five corpus-based open challenges, laying the groundwork for a unified syntactic theory and its deeper integration into BPM research and practice.

This study addresses the challenges of automatically reconstructing BPMN models from unstructured natural language descriptions, including specification heterogeneity, multilingual inputs, and the absence of ground-truth references. To overcome these issues, the authors propose a large language model (LLM)-driven, multi-stage automation pipeline that integrates multilingual translation, SpiffWorkflow-based execution validation, and LLM-guided iterative repair to generate high-quality, executable BPMN 2.0 XML ground-truth corpora. A novel multidimensional similarity evaluation framework—combining structural alignment, type distribution, and semantic embeddings—is introduced to enable fully automated, large-scale BPMN generation and refinement without manual intervention. Evaluated on 750 public process diagrams, the approach successfully constructs 387 validated models with an average reconstruction similarity exceeding 0.75, including approximately 50 near-perfect reconstructions differing only in element naming.

This work addresses the limitation of existing text-to-process modeling approaches, which predominantly focus on control flow while neglecting resource and collaboration perspectives, thereby struggling to generate complete multi-party models. To overcome this, the authors propose a resource-aware generative pipeline that systematically incorporates the resource dimension into large language model (LLM)-driven process modeling for the first time. The method automatically constructs BPMN 2.0 collaboration diagrams from natural language descriptions, explicitly capturing organizational pools, role-based lanes, and inter-organizational message events, and employs an orthogonal layout algorithm for automated diagram arrangement. Experimental results across ten business processes and nine LLMs demonstrate that the approach accurately extracts resource-related information, maintains high control-flow quality, and incurs only minimal runtime overhead, advancing generative process modeling toward more collaborative and resource-complete representations.

This work addresses the limitations of general-purpose large language models in business process automation, where inconsistent functionality, frequent tool-calling errors, and unstable code quality hinder industrial-grade reliability and maintainability. Focusing on the task of translating BPMN diagrams into executable agent workflows, we propose the first specialized agent system designed for structured code generation. By integrating BPMN control-flow semantics, a deterministic path execution mechanism, and a lightweight code generation strategy, our approach achieves high-precision, low-latency, and zero-repair automation. Experimental results demonstrate that, compared to general-purpose models, our method improves tool-calling accuracy by 9–20 percentage points, reduces latency by 2–4×, decreases calling errors by a factor of three, lowers token-generation costs by over 95%, and entirely eliminates the need for repair iterations.