Healthcare risk management remains fragmented, often relying on manual analyses or isolated techniques that provide limited traceability, weak integration with business process representations, and insufficient tool support across the risk management life cycle. This limitation is particularly critical in medication use processes, where adverse drug events emerge from tightly intertwined clinical, organizational, and human factors. To address this gap, this paper presents a substantial model-based extension of the previously published Business Process–Risk Integrated Method (BPRIM) toward a more coherent and executable risk management method, evaluated in a healthcare setting. More specifically, the present BPRIM extension is structured through four coordinated layers: an enriched methodological workflow combining structured risk identification, Bowtie-inspired causal–consequence analysis, semiquantitative risk evaluation, and explicit risk treatment; an extended metamodel for representing safety barriers, causal factors, consequences, and treatment actions; executable semiquantitative algorithms for likelihood propagation, criticality estimation, and comparative prioritization; and an operational implementation within the extended AdoBPRIM prototype, released as AdoBPRIM4health. The extended BPRIM method was subjected to an exploratory pilot evaluation with a multidisciplinary hospital team focusing on medication errors in a geriatric medication use process. Practitioner feedback indicates improved shared understanding of risk scenarios, stronger traceability between causal reasoning and treatment decisions, and more structured multidisciplinary risk discussions. Beyond the healthcare case, the study illustrates how model-driven engineering can support executable and traceable integration of heterogeneous risk management activities within a unified domain-specific method.
Heterogeneous blockchain systems pose significant challenges to seamless cross-chain integration. Supporting business-level atomic recovery is important for cross-chain workflows, as a failure in one blockchain may compromise interdependent operations across multiple chains. Existing cross-chain solutions primarily address token-level atomicity, while business-level atomic recovery remains underexplored. Consequently, developers must manually coordinate smart contracts, data flows, and compensating rollback logic across multiple blockchains, resulting in high development costs and effort. This paper introduces Model-Xchain, a model-driven framework that combines a script-based domain-specific language (DSL) with a Directed Acyclic Graph (DAG) model. The DSL allows developers to specify cross-chain data operations, execution dependencies, conditional logic, and rollback actions using scripts. These scripts are then automatically transformed into executable DAG models, which guide dependency-aware execution and support saga-style compensating rollback after failures. We evaluated Model-Xchain in a comparative seafood supply-chain study using permissioned blockchains. The study focused on execution failures handled by the compensating-rollback mechanism; malicious security attacks were outside its scope. Forty developers implemented the same workflow using Model-Xchain or a BitXHub-based SDK baseline. In the evaluated setting, using Model-Xchain reduced development time by 50–55
Low-cost Internet of Things (IoT) weather stations enhance spatial and temporal coverage for hyperlocal forecasting, especially in remote or hard-to-reach areas where traditional monitoring infrastructure is limited. However, their dependable operation is affected by component reliability, message delivery performance, and energy-related constraints, particularly battery depletion and solar recharge variability. This paper presents a dependability analysis of a real IoT-enabled weather monitoring platform based on a Weather Monitoring Approach (WMA), modeled using Stochastic Petri Nets (SPNs) to evaluate availability and reliability, while explicitly modeling energy autonomy as a cross-cutting operational constraint that affects continuous operation. Results show that the proposed WMA significantly increases operational availability, reduces failure probability, and improves energy autonomy by reducing the likelihood of battery depletion and extending operational continuity. In addition, the optimized communication configuration substantially decreased the latency required for near-certain message delivery, highlighting the impact of transmission tuning on system dependability. The proposed WMA provides a means to analyze configuration and design changes that can further improve system dependability, demonstrating how the combination of reliability modeling, energy autonomy mechanisms, and efficient communication strategies can substantially enhance the dependability of IoT-enabled weather monitoring systems and support continuous operation in regions with limited maintenance accessibility.
Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineering these pipelines remains largely ad-hoc: pipelines are hard to specify, validate, and reuse, with scarce dedicated tooling. Function+Data Flow (FDF) addresses this by defining a visual domain-specific language (DSL) that represents functions (ML models) explicitly, enabling their composition and reuse. We implemented FDF in DesCartes Builder, an integrated modeling environment supporting FDF-based DT synthesis and validation. In this paper, we report on an empirical user study evaluating whether FDF and DesCartes Builder can make AI-based DT development more accessible and reliable. Participants implemented a representative real-time DT prototype within DesCartes Builder, and we measured perceived usability and feature adequacy through quantitative and qualitative measures. Our results indicate that DesCartes Builder and FDF achieve a good level of usability across a broad range of potential users, and particularly for the intended audience of domain experts. The study additionally surfaces concrete strengths and areas for improvement of both the tool and the underlying FDF framework. Informed by these findings, we propose H-FDF, a Hierarchical extension of FDF supporting iterative and modular pipelines, enabling the formal specification of more complex DT pipelines such as dual training. Our findings suggest that integrated, model-driven platforms are a promising direction to transform AI-based DT engineering into a disciplined modeling practice.
Developers spend a large portion of their time on software comprehension, making it essential to understand how different artifacts – models and code – support this activity. While prior research has primarily explored the use of models as complementary aids for understanding code, limited evidence exists regarding their standalone effectiveness in conveying software’s structural and behavioral aspects. This gap is particularly relevant in contexts such as Model-Driven Engineering (MDE) and Low-Code/No-Code (LCNC) approaches, where models often serve as the primary or sole representation of software. The paper reports on two complementary controlled experiments that investigate how models and code contribute to understanding software structure and behavior. The first experiment compares comprehension when only reverse-engineered UML models or source code were available (code-centric settings), mimicking scenarios in agile or AI-assisted development where models are outdated or absent. The second experiment focuses on model-driven settings, examining whether providing generated code alongside platform-independent models improves comprehension and whether differences in code generators influence performance. The results demonstrate that models and code achieve comparable overall correctness, and thus models alone may be sufficient in model-centric scenarios where access to code is limited or unavailable. The experiments also reveal a consistent structure-behavior comprehension gap, show differences in comprehension across tasks regardless of artifact type, and indicate that adding code to models can both clarify and confuse comprehension depending on context.
