Designing Efficient Business Processes in the Metaverse with EffABPMN2MV: A Model-Driven Framework for Collaborative Software Design, AI-Driven Documentation, and Stakeholder Rights Management
2026· Journal of Resource Management and Decision Engineering· Vol 5, pp. 1-16· 0 citations
TL;DR
EffABPMN2MV represents a theoretically grounded and empirically validated contribution to BPM, collaborative software design, and blockchain-enabled information systems.
Abstract
Business process management (BPM) faces three persistent structural challenges: (1) the exclusion of non-technical stakeholders from process design due to the complexity of BPMN 2.0 notation; (2) the fragmentation among requirements engineering, user interface design, and process modeling; and (3) the absence of a transparent mechanism for managing stakeholder intellectual property (IP) rights. This paper introduces EffABPMN2MV — a ten-layer collaborative design ecosystem operating within an immersive metaverse environment. The framework comprises three integrated components: (i) EffABPMN2MV-ML, a modeling language that maps metaverse UI interactions to BPMN 2.0 elements; (ii) EffABPMN2MV-Tools, a GPT-4-powered AI engine that automatically generates four standardized software deliverables; and (iii) EffABPMN2MV-SCB, a Hyperledger Fabric smart contract subsystem that immutably records stakeholder contributions and allocates LNT participation tokens. A controlled experiment with two parallel nine-member teams (Group A: EffABPMN2MV; Group B: Scrum) and an independent expert panel (n=15) was conducted using a banking loan application case study. EffABPMN2MV reduced documentation time by 58.3% (t=4.82, p<.001, Cohen's d=2.28), improved document quality by 21.4% (d=1.65), enhanced stakeholder experience (d=2.27), and raised legal transparency from 2.58 to 4.61 out of 5.0 (d=3.47). All effects were statistically significant (p<.001) with large effect sizes (d>1.0). EffABPMN2MV represents a theoretically grounded and empirically validated contribution to BPM, collaborative software design, and blockchain-enabled information systems.
The proposed model aims to support the formalization of model selection processes, improve decision-making, and enhance the traceability and transparency of LLMOps practices and forms part of a broader research effort toward the formalization of the entire LLMOps life cycle.
Maria Chernigovskaya, A. Nahhas, Christian Haertel et al.· Proceedings of the 21st Inte...· 0 citations
Integrating Large Language Models (LLMs) into the Software Development Life Cycle (SDLC) can improve developer productivity, but it also introduces security, privacy, and compliance risks during model selection. Regulations and frameworks such as the EU AI Act, the NIST AI Risk Management Framework (RMF), the General Data Protection Regulation (GDPR), the Lei Geral de Prote\c{c}\~ao de Dados (LGPD), and ISO/IEC 42001 establish obligations that are often difficult to translate into operational criteria for technical decision-making. This paper proposes a model to support governance and compliance in LLM selection for software engineering projects. The model is developed through Design Science Research (DSR) and is structured in three layers: (i) regulatory requirements, (ii) organizational governance capabilities, instantiated by a multi-criteria decision matrix with knock-out and weighted scoring criteria, and (iii) productivity and sustainability outcomes, operationalized by the LLM governance assessment protocol (PAG-LLM). A regulatory feedback loop connects operational results back to the normative layer, enabling iterative refinement of the model. A pilot evaluation with 20 adversarial scenarios based on Common Weakness Enumeration (CWE) and the OWASP Top 10 suggests distinct risk profiles between commercial cloud-based LLMs and local open-source LLMs. The results provide preliminary evidence that regulatory disqualification logic, particularly K.O. criteria, can prevent the selection of technically competitive models that nonetheless pose unacceptable compliance risks, demonstrating the feasibility of governance-oriented LLM selection in software engineering projects.
J. Quintino, Hermano Moura, Filipe Calegario· 0 citations
Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support.
Zhu Xiang, Minghao Li, Tianyang Lei et al.· Systems· 0 citations
Digitalization and regulatory compliance pose substantial challenges to companies, requiring adjustments to operations and business processes. Smooth transitions can be facilitated by analyzing discrepancies between current and target processes, enabling the identification of necessary organizational changes. Based on these insights, change managers can develop action plans to support effective implementation and ensure return on investment. Although scholars emphasize the importance of data-driven evaluation in change management (CM) and recognize the value of information embedded in business process models, the literature lacks systematic methods for extracting and integrating such information, particularly from text-based sources. In collaboration with industrial partners, we developed a method to address this gap. Our approach integrates semantic business process management, text analytics, and CM to compare process models with industrial standards, align process ontologies, and translate detected deviations into actionable recommendations. The method also resolves terminological inconsistencies across heterogeneous sources. This paper presents an analytics-based framework that delivers practical, context-specific guidance to change managers. To demonstrate applicability, we implemented a proof of concept in an industrial environment to validate process adherence against natural language documents such as industry standards
Domonkos Gáspár, Ildikó Szabó, Katalin Ternai et al.· Journal of Industrial Integr...· 0 citations
Program Increment (PI) Planning is a critical synchronization event in scaled Agile methodologies, yet remains largely informal, with limited analytical rigor or strategic alignment. This paper introduces a formal metamodel and a model-driven framework to support structured PI Planning in large-scale software development. Our metamodel captures essential planning artifacts, including features, teams, iterations, risks, and dependencies, and is enriched with OCL constraints for validation and traceability. We extend the model to align planning with business value streams and introduce stakeholder-specific viewpoints for tailored analysis. Compared to tools like Jira or Mural, our approach provides formal semantics, automated reasoning, and extensibility. This work lays the foundation for a Domain-Specific Language (DSL) for PI Planning, bridging the gap between Agile practice and formal modeling.
Le Program Increment (PI) Planning est un événement de synchronisation essentiel dans les méthodologies d'agilité à l’échelle, mais il reste largement informel, avec une rigueur analytique et un alignement stratégique limités. Cet article présente un métamodèle formel et un cadre fondé sur l’ingénierie dirigée par les modèles (model-driven engineering) pour soutenir un PI Planning structuré dans le développement logiciel à grande échelle.Notre métamodèle capture les artefacts essentiels de planification, tels que les fonctionnalités (features), les équipes, les itérations, les risques et les dépendances, et il est enrichi de contraintes OCL pour la validation et la traçabilité. Nous étendons ce modèle afin d’aligner la planification sur les value streams métier et introduisons des vues spécifiques aux parties prenantes pour une analyse ciblée. Comparée à des outils tels que Jira ou Mural, notre approche fournit une sémantique formelle, un raisonnement automatisé et une extensibilité accrue.Ce travail pose les bases d’un langage spécifique (Domain-Specific Language, DSL) dédié au PI Planning, comblant ainsi le fossé entre la pratique Agile et la modélisation formelle.
F. Somda, D. Guel, Kisito K. Kabore· Revue Africaine de Recherche...· 0 citations
Background: Bridging the gap between requirement analysis and system design remains a critical challenge in software engineering. Existing Problem Frames (PF)–based approaches lack automated mapping mechanisms to Unified Modeling Language (UML) models, resulting in requirements-design disconnection, inefficient manual transformation, and insufficient traceability. Methods: We propose PF2UML, a model-driven transformation method that automatically converts PF specifications into comprehensive UML requirement models. The approach comprises three components: (1) an extended PF meta-model with semantic annotations addressing structural, behavioral, and servicelevel gaps; (2) a graphical modeling platform built on Eclipse Modeling Framework (EMF) and Sirius following the Meta-Object Facility (MOF) three-layer architecture; and (3) 24 declarative transformation rules implemented in Atlas Transformation Language (ATL), generating four UML diagram types: conceptual class diagrams, use case diagrams, service diagrams, and SSDs. Results: Validation on five representative opensource systems with 12 participants demonstrates a transformation accuracy of 93.6% (130% improvement over baseline), an average reduction in end-to-end time from Requirements Modeling and Model Transformation (RM+MT) of 26.8%, and a practical average ROI of 236.6%. Conclusion: PF2UML establishes seamless and traceable integration from requirements modeling to system design, significantly reducing manual effort and improving consistency for complex interdisciplinary system development.
Hongbin Xiao, Zhi Li, Yilong Yang et al.· International journal of sof...· 0 citations