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.
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.
Enterprise modeling (EM) typically includes multiple perspectives to analyze or design an organization, which is supposed to reduce the complexity of the modeling process and improve the structure of the resulting enterprise models. However, for non-modeling experts, i.e., stakeholders without solid training in EM, the development and integration of different model perspectives remains a challenge. Based on previous research in conceptual modeling, the paper investigates the use of large language models (LLMs) to support non-modeling experts of multi-perspective EM. Positioned in the roadmap for increasing the reach of EM, an LLM-based toolchain and reusable prompts for generating multi-perspective enterprise models from textual descriptions are proposed and evaluated. The results indicate that LLMs can be seen as assistive technology for certain tasks in EM. The main contributions of the paper are (1) an analysis of the state of research in LLM use in EM, (2) a study on LLM use for producing multi-perspective enterprise models, and (3) positioning LLMs in the roadmap for increasing the reach of EM.
