Automating Execution and Verification of BPMN+DMN Business Processes
- URL: http://arxiv.org/abs/2512.15214v1
- Date: Wed, 17 Dec 2025 09:10:17 GMT
- Title: Automating Execution and Verification of BPMN+DMN Business Processes
- Authors: Giuseppe Della Penna, Igor Melatti,
- Abstract summary: Most commonly used frameworks to build BPMN+DMN models only allow designers to detect syntactical errors.<n>We provide an experimental evaluation of our methodology on BPMN+DMN processes from the literature.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The increasing and widespread use of BPMN business processes, also embodying DMN tables, requires tools and methodologies to verify their correctness. However, most commonly used frameworks to build BPMN+DMN models only allow designers to detect syntactical errors, thus ignoring semantic (behavioural) faults. This forces business processes designers to manually run single executions of their BPMN+DMN processes using proprietary tools in order to detect failures. Furthermore, how proprietary tools translate a BPMN+DMN process to a computer simulation is left unspecified. In this paper, we advance this state of the art by designing a tool, named BDTransTest providing: i) a translation from a BPMN + DMN process B to a Java program P ; ii) the synthesis and execution of a testing plan for B, that may require the business designer to disambiguate some input domain; iii) the analysis of the coverage achieved by the testing plan in terms of nodes and edges of B. Finally, we provide an experimental evaluation of our methodology on BPMN+DMN processes from the literature.
Related papers
- BPMN to PDDL: Translating Business Workflows for AI Planning [6.865656740940774]
This project builds upon prior theoretical work to develop a functional pipeline that translates BPMN diagrams into PDDL representations suitable for planning.<n>The system supports core BPMN constructs, including tasks, events, sequence flows, and gateways, with initial support for parallel and inclusive gateway behaviour.
arXiv Detail & Related papers (2025-11-22T19:51:23Z) - On the Marriage of Theory and Practice in Data-Aware Business Processes via Low-Code [0.9685837672183747]
This work introduces BPMN-ProX, a low-code testing framework that significantly enhances the verification of data-aware BPMN.<n>This innovative approach combines theoretical verification with practical modeling, fostering more agile, reliable, and user-centric business process management.
arXiv Detail & Related papers (2025-10-31T06:47:06Z) - Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text [75.77648333476776]
This paper introduces an automated pipeline for extracting BPMN models from text.<n>A key contribution of this work is the introduction of a newly annotated dataset.<n>We augment the dataset with 15 newly annotated documents containing 32 parallel gateways for model training.
arXiv Detail & Related papers (2025-07-11T07:25:55Z) - BPMN to Smart Contract by Business Analyst [0.0]
This paper addresses the challenge of creating smart contracts for applications represented using Business Process Management and Notation (BPMN) models.<n>In our prior work we presented a methodology that automates the generation of smart contracts from BPMN models.<n>In subsequent research, we enhanced our approach by adding support for nested transactions and enabling a smart contract repair and/or upgrade.
arXiv Detail & Related papers (2025-05-28T17:28:38Z) - A Step Towards a Universal Method for Modeling and Implementing Cross-Organizational Business Processes [0.0]
This study lays the groundwork for more accurate and unified business process model executions.
It describes the development of a prototype translator that converts specific BPMN elements into a format compatible with PASS.
These models are then transformed into source code and executed in a bespoke workflow environment.
arXiv Detail & Related papers (2024-06-18T06:19:44Z) - Towards Generating Executable Metamorphic Relations Using Large Language Models [46.26208489175692]
We propose an approach for automatically deriving executable MRs from requirements using large language models (LLMs)
To assess the feasibility of our approach, we conducted a questionnaire-based survey in collaboration with Siemens Industry Software.
arXiv Detail & Related papers (2024-01-30T13:52:47Z) - A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems [1.0624606551524207]
We propose a formalization of the execution semantics of BPMN.
Our approach is based on a higher-order transformation from BPMN models to graph transformation systems.
To show the capabilities of our approach, we implemented it as an open-source web-based tool.
arXiv Detail & Related papers (2023-11-09T09:55:10Z) - Just Tell Me: Prompt Engineering in Business Process Management [63.08166397142146]
GPT-3 and other language models (LMs) can effectively address various natural language processing (NLP) tasks.
We argue that prompt engineering can help bring the capabilities of LMs to BPM research.
arXiv Detail & Related papers (2023-04-14T14:55:19Z) - Towards Semantic Communication Protocols: A Probabilistic Logic
Perspective [69.68769942563812]
We propose a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog)
By leveraging its interpretability and memory-efficiency, we demonstrate several applications such as SPM reconfiguration for collision-avoidance.
arXiv Detail & Related papers (2022-07-08T14:19:36Z) - Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution
Detection [55.028065567756066]
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications.
In this paper we propose an uncertainty quantification approach by modelling the distribution of features.
We incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble neural networks (BE-SNNs) and overcome the feature collapse problem.
We show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionM
arXiv Detail & Related papers (2022-06-26T16:00:22Z) - CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT
(Extended Version) [62.96267257163426]
We introduce the CoCoMoT (Computing Conformance Modulo Theories) framework.
First, we show how SAT-based encodings studied in the pure control-flow setting can be lifted to our data-aware case.
Second, we introduce a novel preprocessing technique based on a notion of property-preserving clustering.
arXiv Detail & Related papers (2021-03-18T20:22:50Z) - Process Discovery for Structured Program Synthesis [70.29027202357385]
A core task in process mining is process discovery which aims to learn an accurate process model from event log data.
In this paper, we propose to use (block-) structured programs directly as target process models.
We develop a novel bottom-up agglomerative approach to the discovery of such structured program process models.
arXiv Detail & Related papers (2020-08-13T10:33:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.