The increasing complexity and widespread deployment of smart contracts (SCs) on blockchain platforms have heightened the need for interpretable and verifiable representations. While smart contracts encode critical business logic, their low-level implementations remain difficult for auditors and regulators to interpret. To bridge this semantic gap, we propose a structure-aware instruction-tuning framework that translates Solidity functions into Business Process Model and Notation (BPMN) diagrams using large language models (LLMs). Our approach constructs a high-quality dataset of 15K Solidity-BPMN pairs through embedding-based clustering, prompt engineering, and multi-template augmentation. We fine-tune DeepSeek-Coder using LoRA for efficient domain adaptation, enabling the model to generate syntactically valid and semantically faithful BPMN structures. Experimental results show that our fine-tuned model outperforms GPT-4o, Gemini, and baseline LLMs in both structural precision and semantic fidelity. This work lays the groundwork for structure-level explainability of smart contracts and supports future research in code-to-process modeling and blockchain compliance analysis.
Flavio Corradini, Alessio Galassi, Alessandro Marcelletti, Barbara Re
Blockchain has been largely adopted in several sectors through decentralized applications. These rely on smart contracts, whose implementation can diverge from the intended logic, leading to unexpected behaviors. Such behaviors can be identified by observing the data produced within smart contracts’ execution, employing compliance checking techniques. Their adoption poses a main limitation since the traditional rule specification languages do not explicitly refer to the blockchain. To address this limitation, we propose a domain-specific language, called CoBlock , to define compliance rules by relying on blockchain characterizations as a first-class citizen. This enables a tailored framework for compliance checking, supporting the definition of rules to check smart contract execution data. The framework is implemented as a web application. We demonstrate and evaluate the applicability of the language and the accuracy and feasibility of the framework using two real-world decentralized applications, namely Augur and PancakeSwap.
Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering.
Javier Godoy, Margarita Capretto, MartÃn Ceresa, Juan Pablo Galeotti · 7 authors
Smart contracts manage valuable assets, and their immutability hinders bug fixing. Therefore, pre-deployment verification and validation are critical. In fact, auditing has become mandatory in the pipeline of smart contract development. Auditors usually combine manual inspection with automated tools in their auditing work, looking for issues that may be domain dependent (i.e., pertaining to the correct implementation of requirements-which are often informal, partial, and implicit) or independent (e.g., reentrancy, overflow, etc.), To identify domain dependent issues, it is important to understand the non-trivial behavior of the implementation over sequences of calls made by callees playing different roles in the contract. In this paper, we propose a novel approach that combines predicate abstraction with modal transition systems to build abstractions that can help auditors in the smart contract validation process. The required inputs are a set of predicates provided as code and, optionally, constraints over smart contract function parameters. The output is a modal transition system that captures the contract's behavior. We report on a prototype that builds modal abstractions and an evaluation on two established benchmarks where we identified four previously unreported issues.
Interoperability between blockchain platforms remains a key challenge, particularly in sensitive domains such as healthcare, where the secure and consistent exchange of clinical information between institutions is essential. While technical interoperability solutions exist, semantic interoperability at the level of smart contracts continues to be a significant limitation. This paper presents MUISCA, a mechanism based on Model-Driven Engineering that enables the automatic generation of interoperable smart contracts across different blockchain platforms. By defining metamodels, abstract models, and transformation rules, MUISCA produces platform-specific code for technologies such as Ethereum and Hyperledger Fabric. The mechanism was validated through a healthcare case study focused on patient transfers between medical institutions, demonstrating its ability to support the secure exchange of clinical data. Additionally, its acceptance was evaluated through expert surveys assessing perceived usefulness and ease of use. Results show that MUISCA improves smart contract portability, reduces implementation errors, and enhances system security. The proposed solution contributes to advancing semantic interoperability in blockchain-based health information systems and provides a foundation for broader application in other critical domains that require high levels of integration and data protection.
Ensuring the integrity of business processes without disclosing confidential business information is a major challenge in inter-organizational processes. This paper introduces a zero-knowledge proof (ZKP)-based approach for the verifiable execution of business processes while preserving confidentiality. We integrate ZK virtual machines (zkVMs) into business process management engines through a comprehensive system architecture and a prototypical implementation. Our approach supports chained verifiable computations through proof compositions. On the example of product carbon footprinting, we model sequential footprinting activities and demonstrate how organizations can prove and verify the integrity of verifiable processes without exposing sensitive information. We assess different ZKP proving variants within process models for their efficiency in proving and verifying, and discuss the practical integration of ZKPs throughout the Business Process Management (BPM) lifecycle. Our experiment-driven evaluation demonstrates the automation of process verification under given confidentiality constraints.
Real-World Assets (RWAs) serve as a bridge between traditional financial instruments and decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can extend the scope of decentralized finance. Industry forecasts further indicate rapid growth in tokenized RWAs after 2025, underscoring their potential role in the evolution of digital financial markets. However, in the current multi-chain environment, RWAs face challenges such as repeated authentication across multiple chains and inefficiencies arising from multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and cross-chain interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, this paper adopts a cross-chain channel that supports efficient RWA settlements, and we refine its design so that the channel does not need to be closed immediately after each settlement, thereby reducing on-chain cost. We implement the framework and evaluate its performance via simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
Aleksandr Kormiltsyn, Sowelu Avanzo, Vimal Dwivedi, Alex Norta · 5 authors
This paper explores conflict resolution in decentralized e-health prescription creation workflows, necessary for secure and efficient multi-stakeholder data sharing. Consensus mechanisms ensure consistency and enable trust across distributed systems. Decentralized Autonomous Organizations (DAOs) are adopted for decentralized decision-making in several domains but remain unexplored in e-healthcare. Current consensus mechanisms lack integration with governance models, limiting their adaptability to domain-specific requirements. Moreover, there is no existing consensus algorithm adapted for e-health, resulting in the lack of privacy, interoperability, and patient-centered data ownership. As a result, automatic conflict resolution in interorganizational e-health processes is complicated or almost impossible. To address this gap, we propose a domain-specific consensus algorithm adapted to the requirements of the e-health domain. The algorithm is embedded within a DAO-based governance framework, enabling transparency in decision-making among e-health stakeholders. Such integration enables automated, privacy-preserving conflict resolution in interorganizational e-health workflows. Following the Design Science methodology, the consensus algorithm for e-health DAO is based on stakeholder-driven requirements and evaluated using Colored Petri Nets (CPN). The evaluation shows the solution improves conflict resolution enabling fair, efficient, and privacy-aware collaboration in decentralized e-health.
Decentralized Autonomous Organizations (DAOs) are a class of Decentralized Applications (DApps) using smart contracts to facilitate governance processes. The design of DAOs is affected by additional complexity compared to other DApps due to the need to specify organizational roles, permissions, and control relations early in the early development stages of the system. In addition, DAOs face scalability challenges. While existing Model-Driven Development (MDD) tools support general smart contract and DApp design, they lack constructs tailored to the organizational and governance features unique to DAOs. To bridge this gap, we develop a code generation approach for DAO-ML, a visual modeling language for DAO design. The translator we implement for this method generates smart contracts of DAOs with suitably configured roles and permissions from visual models. The generated smart contracts particularly optimize the representation of roles and permissions to improve the system scalability and handle complex governance structures, necessary to increase the utility of DAO systems. The approach is evaluated in the context of an in vivo case study on the development of a DAO for the disintermediated management of local tourism. This work advances MDD for decentralized systems by bridging high-level governance modeling with executable, gas-efficient smart contract code generation.
The use of Enterprise Data Warehouse (EDWs) has been experienced as the analytical backbone of risk management, financial reporting and regulatory reporting of the data in very regulated sectors like banking, insurance, and capital markets. They were based on batch-oriented Extract Transform Load (ETL) paradigms, tight coupled schema and monolithic governance models that are better suited to stability than agility. Nevertheless, the increasing regulatory complexity, impacts of the near-real time risk visibility requirements, and increasing cost of infrastructure have emanated inherent weaknesses of the legacy EDW architectures. At the same time, the emergence of hybrid cloud platforms, scalable object storage, distributed query engines, and workflow orchestration system has made it possible to make the paradigm shift toward Extract–Load–Transform (ELT), domain-driven data products, and decentralized ownership models. In spite of these developments, in numerous organizations, the pressure to modernize reporting pipes based on strong backward compatibility criteria, audit limitations and the operational risks of massive data migrations makes this a challenge. This paper gives a detailed blueprint of modernization in the process of moving the old EDW centric ETL architectures to the hybrid cloud ELT platforms to suit the risk, finance, and regulatory reporting. Its proposed solution integrates domain-driven data products and ELT pushdown transformations orchestrating control planes and explicit data contracts that is applied in an incremental fashion with a strangler pattern. The framework focuses on retrogressively compatible schemas, reconcilability determinacy, the rollback safety nets, and regulated cutover plans to provide continuous regulatory compliance. Using a well-organized migration roadmap, cost and performance metrics and an official risk register, the paper will show how organizations can shorten report delivery cycles, enhance service-level agreement (SLA) compliance and minimize the overall cost of ownership without sacrificing auditability and strict governance. The findings have shown that hybrid cloud ELT systems may cut the latency in report by more than 40%, cut compute expenditure by up to 35, and become much more responsive to regulatory cases without infection of information integrity or resilience.
One major issue associated to microservices architectures is aspects related to data consistency and management of data within such a context. To measure the effect on system integrity, this research paper compares different data consistency models which are, eventual consistency, distributed transactions through Two-Phase Commit (2PC), the Saga pattern. Employing historical data analysis, case studies, and examining the recent technological advancements, the study gives a comparative assessment of various approaches in terms of their capabilities to achieve transaction latencies and throughput and possibility to guarantee consistent results. Based on the results, it is evident that although eventual consistency models provide high scale-out and availability characteristics, the models experience transitory data inconsistency. Consistency is high since data is accessed from, and committed to, multiple databases in a distributed transaction but they have performance penalties. The Saga patterning gives a balanced view but at the same time brings together additional design difficulty. New trends like Distributed Ledger Technology (DLT) as well as new consensus algorithms that can be used to solve problematic approaches like the Raft algorithm give new possibilities for improving consistency of the data but bring their own concerns. The study implies that one must choose the right consistency model depending on the need and performance Degree and the future research can consider the use of the combination of several models along with the improvement of technological solutions to have a better deal with data consistency in microservices.
As digital transformation accelerates, data has become a core driver of technological innovation and economic growth. However, a key challenge in data utilization is the semantic misalignment between data supply and the demands of business scenarios. This misalignment significantly hinders efficient data flow and collaborative utilization. To address this issue, this article proposes a federated service solution integrating blockchain and decentralized autonomous organizations and operations (DAOs), large language models (LLMs) and scenarios engineering, federated learning and edge computing, as well as encryption technologies and privacy-computing. A five-layer federated service framework is introduced, consisting of the foundation layer, the data-scenario layer, the semantic coordination layer, the incentive-security layer, and the application layer, which is designed to ensure efficient and context-aware data supply–demand matching while preserving privacy and scalability. Moreover, the core mechanisms for semantic coordination are proposed, and a detailed solution process for resolving semantic misalignment with these mechanisms, as well as an illustrative example, is also presented. The proposed federated service framework offers an effective solution to semantic misalignment in supply–demand matching, fostering seamless data collaboration across diverse business scenarios. This work provides an intelligent service paradigm that leverages distributed data co-governance to address semantic challenges in the digital economy.
With the globalization of the software industry, requirements traceability has become increasingly critical in the software development process. However, the development of large-scale, complex software systems by cross-organizational research teams often faces challenges due to diverse organizational backgrounds, multi-site environments, conflicting objectives, and organizational boundaries. These factors can lead to trust issues, complicating the implementation of requirements traceability. To address these challenges, this study proposes a Smart Contract-Based Requirements Traceability (SCRT) framework. Smart contracts, which are executable code deployed on a blockchain, exhibit properties such as enforceability, tamper resistance, and verifiability. These characteristics empower the SCRT framework to enhance collaboration, communication, and trust among stakeholders while potentially improving the efficiency and quality of software development. Within the SCRT framework, a novel Requirements Traceability Information Model (RTIM) is introduced, which categorizes the links between new and existing artifacts. This model serves as a guide for the smart contract module, delineating which software artifacts to trace and the relationships to establish.
The rapid emergence of decentralized finance (DeFi) has challenged traditional regulatory paradigms by eliminating central intermediaries and introducing borderless, autonomous financial transactions. As regulatory bodies struggle to enforce compliance in these fluid environments, current centralized RegTech solutions reveal inherent limitations in scalability, trust, and fault tolerance. This paper proposes a novel, real-time compliance verification framework using a decentralized multiagent system integrated with blockchain infrastructure. The system models regulatory rules using formal logic and distributes them across autonomous agents capable of real-time decisionmaking, trust-weighted consensus, and privacy-preserving validation through cryptographic techniques. Smart contracts ensure immutable auditability, while agent roles—validator, regulatory, and observer—coordinate seamlessly using FIPA-compliant communication protocols. Experimental evaluation demonstrates over 98 % compliance accuracy under normal conditions, sub200 ms verification latency, and resilience to agent dropouts and adversarial attacks. By removing single points of failure and enabling scalable, cross-jurisdictional rule enforcement, this framework offers a robust and transparent compliance solution for the next generation of decentralized financial systems. The approach sets a new benchmark in embedding regulatory intelligence within blockchain-based fintech, aligning technical scalability with legal accountability.
Shuning Sun, Xi Ran, Shuchao Pang, Xiaofeng Chen · 5 authors
In Web 3.0, to achieve the continuous release of the value of data elements, we start with technologies related to data circulation and conduct research and analysis on the current data element market. We find that in the traditional model of data element circulation, the sharing and exchange of multi-source heterogeneous data are disconnected from the circulation and release of data element value. This results in poor data reusability, unclear data requirements, small market size, short lifecycle of data elements and data markets, making it difficult to motivate sellers to actively participate in building the data element market, and thus hard to achieve a positive cycle of data element value release. To address this, we have conducted a strategic analysis of the value release of data elements and designed a universal reference model, DataR2E, for the entire process of value release in the data elements market. Within the DataR2E model, we introduce the concept of data element bounty production, utilizing Web3 technology and data production tools to encourage sellers to actively provide the data elements needed by buyers. We envision using data production tools to build a bridge between buyers and sellers in the data elements market, promoting the sustainable development of the data element market, overcoming the mismatch in data elements expected by buyers and sellers, thereby resolving the issues of market presence without pricing and pricing without market presence, achieving sustainable development of the data elements market, and maximizing the potential of data element value release.
Open-source software (OSS) projects face significant challenges in establishing fair and inclusive value distribution models that appropriately reward all contributors. This study employs design science research to develop meta-requirements and three design principles for a blockchain-based solution addressing these challenges. Using multi-grounded theory, we analyze data from in-depth interviews with OSS practitioners, complemented by social media content analysis, to examine value distribution, governance, and collaboration in OSS ecosystems. By integrating empirical findings with Decentralized Autonomous Organization (DAO) governance mechanisms, we derive three design principles for a sustainable governance framework that enables systematic value distribution in OSS projects while enhancing project collaboration. Our findings contribute to both theory and practice by providing actionable guidelines for implementing DAO-based governance in OSS environments.
In increasingly dynamic organizational and computational environments, workflows controlled by multi-agent systems face often face context changes, blurred task borders, and unexpected behaviors. This research proposes a new model for coping with decision drift in adaptive workflows by modeling latent boundary negotiation. By using concepts from complex systems theory, cognitive modeling, and distributed artificial intelligence, we define agents as autonomous systems which are able to perceive, report, and re-negotiate internal role expectations through localized negotiation protocols. The designed model merges drift detection with multi-agent coordination techniques and correlates them to simulated workflow processes typical for high risk, time constrained settings like crisis management, distributed logistics, and agile development teams. Simulation results show that latent negotiation improves task coherence, decreases agent interference, and stabilizes the performance of workflows under ambiguous and changing boundary conditions much more than negotiation less systems. The benefits of negotiation-aware multi-agent systems in supporting sustained alignment of decisions and flexibility in the system are highlighted through comparison with static and rule-based systems. The results outline latent boundary negotiation as a key feature towards the implementation of intelligent self-organizing work systems in decentralized multi agent ecosystems.
Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to embed natural language messages into transactions. Users can leverage these Input Data Messages (IDMs) for peer-to-peer communication. This means that, beyond Ethereum's well-known role as a financial infrastructure, it also serves as a decentralized communication medium. We present the first large-scale analysis of Ethereum IDMs from the genesis block to February 2024 (3134 days). We filter IDMs to extract 867,140 transactions with informative IDMs and use LLMs for language detection. We find that English (95.4%) and Chinese (4.4%) dominate the use of natural languages in IDMs. Interestingly, English IDMs center on security and scam warnings (24%) with predominantly negative emotions, while Chinese IDMs emphasize emotional expression and social connection (44%) with a more positive tone. We also observe that longer English IDMs often transfer high ETH values for protocol-level purposes, while longer Chinese IDMs tend to involve symbolic transfer amounts for emotional intent. Moreover, we find that the IDM participants tend to form small, loosely connected communities (59.99%). Our findings highlight culturally and functionally divergent use cases of the IDM channel across user communities. We further examine the security relevance of IDMs in on-chain attacks. Many victims use them to appeal to attackers for fund recovery. IDMs containing negotiations or reward offers are linked to higher reply rates. We also analyze IDMs' regulatory implications. Their misuse for abuse, threats, and sexual solicitation reveals the urgent need for content moderation and regulation in decentralized systems.
This paper addresses the challenge of creating smart contracts for applications represented using Business Process Management and Notation (BPMN) models. In our prior work we presented a methodology that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN flow control, making it independent of the underlying blockchain infrastructure, with only the BPMN task elements requiring coding. In subsequent research, we enhanced our approach by adding support for nested transactions and enabling a smart contract repair and/or upgrade. To empower Business Analysts (BAs) to generate smart contracts without relying on software developers, we tackled the challenge of generating smart contracts from BPMN models without assistance of a software developer. We exploit the Decision Model and Notation (DMN) standard to represent the decisions and the business logic of the BPMN task elements and amended our methodology for transformation of BPMN models into smart contracts to support also the generation script to represent the business logic represented by the DMN models. To support such transformation, we describe how the BA documents, using the BPMN elements, the flow of information along with the flow of execution. Thus, if the BA is successful in representing the blockchain application requirements using BPMN and DMN models, our methodology and the tool, called TABS, that we developed as a proof of concept, is used to generate the smart contracts directly from those models without developer assistance.
Non-fungible tokens (NFTs) have gained immense popularity and value in recent years. After the Web 3 revolution, many new products have been constructed based on smart contracts or powerful software agreements. One of the most exciting products of Web 3 is NFT, with their increasing popularity, high volatility, and significant price movements making them popular in trading activities. Although many people, investors and researchers perceive NFTs as a trading product, the smart contract and metadata, both of which make up of NFT structure, offer valuable support for the big data storage and processing fields. In recent years, researchers have proposed different uses of NFTs in healthcare systems to improve the quality of services by using NFTs’ versatile data storage architecture. In this study, the capability of NFTs in the storage of health data is investigated, the usage and usage proposals of NFTs in health are examined, and probable usage areas in health sciences are evaluated. The uniqueness of this study is, using NFTs as the dead parental data storage in which the metadata does not need to be mitigated, but the stored data is very valuable for doctors to assess the genetic illnesses comes from parental DNAs.
Julie Saesen, Bastian Kindermann, Darius Abel, Steffen Strese
Decentralized autonomous organizations (DAOs) leverage blockchain technology to facilitate collaboration and decentralize decision-making through rules encoded in smart contracts. DAOs challenge traditional governance theory by combining ownership and management using governance tokens. A governance token’s performance is traceable based on token price changes and subject to influencing factors. Understanding these factors is essential to ensure DAOs’ long-term viability as a new organizational form. However, insights into the drivers of token performance are still limited, leaving both DAO founders and potential token holders in the dark about which on-chain governance design choices to adopt to attract potential token holders. We apply signaling theory to elucidate how on-chain governance design choices influence token performance and use a unique dataset of 204 DAOs. We find that a minimum token quorum positively relates to token performance, while embedding voting incentives, such as non-fungible tokens or additional governance tokens, can negatively relate to token performance. Moreover, we identify founders’ stake as a contingent factor moderating these signals.
The increasing demand for scalable blockchain has driven research into parallel execution models for smart contracts. Crystality is a novel smart contract programming language designed for parallel Ethereum Virtual Machines (EVMs), enabling fine-grained concurrency through Programmable Contract Scopes and Asynchronous Functional Relay. This paper presents the first formal structural operational semantics for Crystality, providing a rigorous framework to reason about its execution. We mechanize the syntax and semantics of Crystality in the theorem-proving assistant Coq, enabling formal verification of correctness properties. As a case study, we verify a simplified token transfer function, demonstrating the applicability of our semantics in ensuring smart contract correctness. Our work lays the foundation for formally verified parallel smart contracts, contributing to the security and scalability of blockchain systems.