This paper presents a reference architecture designed to integrate computable smart contracts with existing blockchain platforms. The proposed architecture addresses key challenges in the development and deployment of smart contracts, including security, interoperability, and compliance. A central feature of the RA is the introduction of a multichain adapter, which enables seamless deployment of smart contracts across different blockchain environments. By providing a standardized framework, the architecture enhances modularity, accelerates development, and reduces costs while ensuring robust security and regulatory compliance. The main goal of the architecture is facilitating cross-chain deployment and operations of computable smart contracts.
Mohamad Sadegh Sangari, Kar Wai So, Atefeh Mashatan
Purpose Blockchain technology (BT) presents a decentralized approach that has promising potentials to alleviate many of the long-lasting risks and inefficiencies in trade finance (TF) and supply chain finance (SCF) operations, providing international traders greater access to working capital. Despite this, the actual adoption of the technology and related issues in this space has remained under-researched. This paper examines the state of the practice to identify the main drivers and inhibitors faced by TF/SCF parties in their BT adoption efforts. Design/methodology/approach This exploratory study applies a multi-stakeholder perspective and a mixed-methods approach using semi-structured interviews with practitioners in various stages of BT implementation in TF/SCF initiatives across North America, Europe and Asia. The study then determines the priority of the identified factors using the Bayesian best-worst method (BWM). Findings The findings show that while the discussion has focused on the technological drivers of BT adoption for TF/SCF, practitioners rely more on non-technological factors such as peer adoption and fostering innovation. The findings also reveal how practitioners address common BT issues, including scalability and interoperability. Originality/value The study offers insights into important requirements for realizing the full benefits of BT in support of TF and SCF from an extended technology-organization-environment (TOE) perspective. On a more general level, it highlights what is required to transform this industry toward digitization.
As a new paradigm in the Web3 era, Decentralized Autonomous Organizations (DAOs) not only embodie the spirit of decentralization and collective governance, but also are the forefront of promoting community-led innovation. However, DAOs face challenges in sustainable growth and scalability in community governance, which are closely related to the income distribution model and member participation. Therefore, the Artificial systems, Computational experiments, Parallel execution (ACP) approach is applied to optimize the contribution evaluation and incentive feedback of member behavior through parallel governance and decision-making methods, so as to improve the intelligence of the DAO incentive mechanism. On this basis, the long short-term memory network (LSTM) and combinatorial game theory are used to conduct experimental verification on information sharing within the community. The experimental results show that our proposed method can not only achieve a high degree of information sharing in the community faster than other methods, but also has the ability of autonomous dynamic adjustment. It is of great significance and value to the community governance research and scenario implementation of DAOs.
Fei Lin, Tao-Ni Zhang, Siji Ma, Jun Huang · 6 authors
This paper provides an in-depth analysis of the application of the Quadratic Funding (QF) mechanism in blockchain-based Decentralized Autonomous Organizations (DAOs) of the QF mechanism. Then, the empirical analysis on the influence of QF on promoting project funding is examined, taking the first round of BNB funding on the DoraHacks platform as the exemplar. The focus has been put on its performance on community voting and fund allocation as well as its real-world impact. Finally, based on the analysis results, the paper proposes improvements to the QF mechanism and presents forward-looking strategies for applying Large Language Models (LLMs) in decentralized decision-making. These insights provide valuable perspectives for understanding and optimizing Decentralized Finance (DeFi).
Altering the smart contract deployed on a blockchain is typically a cumbersome task, necessitating a proxy design, secondary data storage, or the use of special APIs. This can be substantially simplified if the programming language features orthogonal persistence, automatically retaining the native program state across program version upgrades. For this purpose, a customized compiler and runtime system needs to arrange the data in a self-descriptive portable format, such that new program versions can pick up the previous program state, check their compatibility, and support implicit or explicit data evolutions. We have implemented such advanced persistence support for the Motoko programming language on the Internet Computer blockchain. This not only enables simple and safe persistence, but also significantly reduces the cost of upgrades and data accesses.
Oct 17, 2024·Companion Proceedings of the 2024 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity
Blockchain technologies are applied in diverse domains such as financial systems, supply chains, and identity management, leading to the emergence of various smart contract languages design. These contracts often involve time dependent transactions recorded immutably on the blockchain, making their correctness crucial. This paper addresses the formal verification of temporal behaviors in smart contracts without human interaction. We study 9 recent smart contract languages used in 7 leading blockchains and model 27 common temporal patterns from 3148 benchmarks across 9 domain specific application categories. We introduce VESC, a temporal specification language that allows developers to specify temporal properties in structured natural language, which VESC compiles into formal linear temporal logic. Our experiments demonstrate that VESC effectively specifies common temporal behaviors, paving the way for automated temporal verification of smart contracts.
While many studies show that more advanced LLMs excel in tasks such as mathematics and coding, we observe that in cryptocurrency trading, stronger LLMs sometimes underperform compared to weaker ones. To investigate this counterintuitive phenomenon, we examine how LLMs reason when making trading decisions. Our findings reveal that (1) stronger LLMs show a preference for factual information over subjectivity; (2) separating the reasoning process into factual and subjective components leads to higher profits. Building on these insights, we propose a multi-agent framework, FS-ReasoningAgent, which enables LLMs to recognize and learn from both factual and subjective reasoning. Extensive experiments demonstrate that this fine-grained reasoning approach enhances LLM trading performance in cryptocurrency markets, yielding profit improvements of 7\% in BTC, 2\% in ETH, and 10\% in SOL. Additionally, an ablation study reveals that relying on subjective news generates higher returns in bull markets, while focusing on factual information yields better results in bear markets. Code is available at https://github.com/Persdre/FS-ReasoningAgent.
Krzysztof Gogol, Manvir Schneider, Tessone, Claudio, Livshits, Benjamin
Layer-2 (L2) blockchains inherit Ethereums security guarantees while reducing gas fees. As a result, they are gaining traction among traders at Automated Market Makers (AMMs), sparking debate over whether they contribute to liquidity fragmentation of Ethereum. Our research suggests that such fragmentation is not currently occurring. However, it could emerge in the future, particularly if Liquidity Providers (LPs) recognize the higher returns available on L2s. Using Lagrangian optimization, we develop a model for optimal liquidity allocation across AMMs on Ethereum and its L2s, using staking as a benchmark. We show that, in equilibrium, AMM liquidity provision returns converge to this reference rate. Additionally, we measure the elasticity of trading volume with respect to Total Value Locked (TVL) in AMMs and find that, on well-established blockchains, an increase in TVL does not necessarily lead to higher trading volume. Finally, our empirical findings reveal that Ethereums liquidity pools are oversubscribed compared to those on L2s and often yield lower returns than staking Ether. LPs could maximize their rewards by reallocating more than two-thirds of their liquidity to L2s and staking.
The integration of blockchain technology, particularly Ethereum and its smart contract, has revolutionized software programming. Solidity, Ethereum’s main language, is crucial due to its features for blockchain applications. However, the immutable nature of Smart Contract (SC) presents significant security issues, with vulnerabilities leading to financial risks. Meanwhile, Large language models (LLMS ) have transformed software development by enhancing coding efficiency and error detection. Despite their potential, current benchmarks often overlook niche languages like Solidity. This paper introduces the first benchmark to evaluate LLMs in Solidity smart contract generation, aiming to improve automated SC development and blockchain deployment reliability using a Test-Driven Development inspired methodology and pass@k metric. This work not only addresses a significant gap in LLM evaluation for blockchain applications but also extends the capabilities of LLMs in this specialized and critical area of software development.
While Non-Fungible Tokens (NFTs) emerged as the common application of blockchain technology, there is a lack of detailed literature around the concepts of NFTs. This study aims to 1) address the contributing factors for the valuation of NFTs and correlation, 2) survey existing legal framework relating to NFTs, 3) discuss the role of regulators and 4) examine perception. This review includes research articles published in Engineering Village, IEEE Xplore, Scopus, Web of Science, ScienceDirect, Business Source Complete, and ABI/INFORM Global databases. The keyword “Non-Fungible Token” generated over two thousand of research articles, but only English-language peer-reviewed journals being selected for an initial pool of 362 journals. Finally, 22 articles were selected by following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocols. The articles were categorized into three areas: valuation, regulation, and perception of NFTs. The results of a detailed analysis of the three areas consisted of 16 studies on valuation, 4 on regulation, and 2 on perception. These studies highlighted 1) the historical prices of NFTs as a key predicting factor for future pricing , 2) legal copyright and shared ownership issues, and 3) digital vs physical perception of objects. This initial analysis conducted one keyword due to the exploratory nature of this research is a limiting factor in the results obtained. Nonetheless, the results provide sufficient indicators that additional analysis of NFTs and its other contributing factors would be of value to the understanding and adoption of these emerging technologies for both researchers and practitioners.
Although many tools have been developed to detect anomalies in smart contracts, the evaluation of these analysis tools has been hindered by the lack of adequate anomalisticreal-world contracts(i.e., smart contracts with addresses on Ethereum to achieve certain purposes). This problem prevents conducting reliable performance assessments on the analysis tools. An effective way to solve this problem is to inject anomalies intoreal-world contractsand automatically label the locations and types of the injected anomalies.SolidiFI, as the first and only tool in this area, was developed to automatically inject anomalies into Ethereum smart contracts. However,SolidiFIis subject to the limitations from its methodologies (e.g., its injection accuracy and authenticity are low). To address these limitations, we propose an approach calledSCAnoGenerator.SCAnoGeneratorsupports Solidity 0.5.x, 0.6.x, 0.7.x and enables automatic anomaly injection for Ethereum smart contracts via analyzing the contracts’ control and data flows. Based on this approach, we develop an open-source tool, which can inject 20 types of anomalies into smart contracts. The extensive experiments show thatSCAnoGeneratoroutperformsSolidiFIon the number of injected anomaly types, injection accuracy, and injection authenticity. The experimental results also reveal that existing analysis tools can only partially detect the anomalies injected bySCAnoGenerator.
This chapter presents a research perspective that explores the transformative impact of blockchain technology on Behavioral and Experimental Economics. It addresses critical digital challenges such as subject identity verification and privacy, trust in researchers, and the design of experimental incentives. By advocating for a blockchain-integrated framework, the chapter aims to enhance data authenticity, privacy, and incentivization through decentralized mechanisms and smart contracts, thereby ensuring research that is transparent, tamper-proof, and practical. Additionally, the chapter proposes a paradigm shift toward a “play to learn” model, which bridges decentralized science with the realm of gaming finance to advance research and development. This integration signals a new era of interdisciplinary research, offering profound insights into human behavior within the digital economy and illuminating new research pathways that connect Web2 to Web3 environments.
Blockchain is a new technology that has received increasing attention in recent years, and its application market prospects are very broad. Currently, more and more enterprises, institutions, research institutions, and individuals have begun to explore the possibility of blockchain applications. FISCO BCOS has released version 3.0 and promoted this version globally. Version 3.0 supports richer block data types, enhances performance and security features, and provides basic consortium chain monitoring and management tools, etc. With the continuous development of FISCO BCOS, it has become one of the largest and most active consortium chain ecosystems in the world. We have developed a carbon asset management system based on the FISCO BCOS consortium chain technology. The system realizes business processes such as registration, trading, and accounting of carbon emission rights, is completely decentralized, ensures data security and trustworthiness, and also improves processing efficiency. The system uses smart contract technology to record all transaction records on the blockchain, ensuring the openness, transparency, and immutability of contracts. At the same time, the system also includes data statistics and report analysis functions, which facilitate users to query and analyze carbon emissions. By using this carbon asset management system, enterprises can better manage their own carbon emissions, achieve green and low-carbon development, and promote sustainable development.
Smart contracts are automated programs that run on blockchain platforms and are a crucial component of blockchain technology. Due to their involvement in significant business logic and asset value, the security and reliability of smart contracts is critical. Fuzzing is an effective method for detecting vulnerabilities in smart contracts. However, most existing fuzzers face a common limitation in efficiently satisfying hard-to-cover branch constraints. To address this challenge, this paper introduces Erinys, a fuzzing tool based on sfuzz. We incorporate the definition and usage of state variables to define hard-to-cover branch constraints. Then we employ static analysis techniques to obtain the Define-Use relationships between functions and state variables in smart contracts. Additionally, we construct a graph, known as the State variables Define Use Graph, to describe the logical connections between functions based on state variables. Based on this graph, we design strategies to generate and optimize function invoke sequences. We implemented Erinys by integrating the proposed method into sfuzz. Experimental results conducted on a dataset containing 1970 contracts demonstrate that Erinys can effectively generate high-quality function invoke sequences, leading to improved branch coverage and vulnerability detection capabilities.
Smart contracts mostly written in Solidity are Turing-complete programs executed on the blockchain platforms such as Ethereum. To prevent resource abuse, a gas fee is required when users deploy or invoke smart contracts. Although saving gas consumption has received much attention, no work investigates the effect of function dispatch on the invocation gas consumption. In this paper, after demystifying how the function dispatch affects the invocation gas consumption, we present FunRedisp, a bytecode refactoring method and an open-source tool, to reduce the overall invocation gas consumption of smart contracts. At the source code level, FunRedisp initially identifies hot functions in a smart contract that have a big chance to be invoked, and then move them to the front of the function dispatch at the bytecode level. We implement FunRedisp and evaluate it on 50 real-world smart contracts randomly selected from Ethereum. The experimental results demonstrate that FunRedisp can save approximately 125.17 units of gas per transaction with the compilation overhead increased by only 0.37 seconds.
Background: Health 3.0 allows decision making to be based on longitudinal data from multiple institutions, from across the patient's healthcare journey. In such a distributed setting, blockchain smart contracts can act as neutral intermediaries to implement trustworthy decision making. Objective: In a distributed setting, transmitted data will be structured using standards (such as HL7 FHIR) for semantic interoperability. In turn, the smart contract will require interoperability with this standard, implement a complex communication setup (e.g., using oracles), and be developed using blockchain languages (e.g., Solidity). We propose the encoding of smart contract logic using a high-level semantic Knowledge Graph, using concepts from the domain standard. We then deploy this semantic KG on blockchain. Methods: Off-chain, a code generation pipeline compiles the KG into a concrete smart contract, which is then deployed on-chain. Our pipeline targets an intermediary bridge representation, which can be transpiled into a specific blockchain language. Our choice avoids on-chain rule engines, with unpredictable and likely higher computational cost; it is thus in line with the economic rules of blockchain. Results: We applied our code generation approach to generate smart contracts for 3 health insurance cases from Medicare. We discuss the suitability of our approach - the need for a neutral intermediary - for a number of healthcare use cases. Our evaluation finds that the generated contracts perform well in terms of correctness and execution cost ("gas") on blockchain. Conclusions: We showed that it is feasible to automatically generate smart contract code based on a semantic KG, in a way that respects the economic rules of blockchain. Future work includes studying the use of Large Language Models (LLM) in our approach, and evaluations on other blockchains.
Daniele Friolo, Geoffrey Goodell, D. R. Toliver, Hazem Danny Nakib
This article builds upon the protocol for digital transfers described by Goodell, Toliver, and Nakib, which combines privacy by design for consumers with strong compliance enforcement for recipients of payments and self-validating assets that carry their own verifiable provenance information. We extend the protocol to allow for the verification that reissued assets were created in accordance with rules prohibiting the creation of new assets by anyone but the issuer, without exposing information about the circumstances in which the assets were created that could be used to identify the payer. The modified protocol combines an audit log with zero-knowledge proofs, so that a consumer spending an asset can demonstrate that there exists a valid entry on the audit log that is associated with the asset, without specifying which entry it is. This property is important as a means to allow money to be reissued within the system without the involvement of system operators within the zone of control of the original issuer. Additionally, we identify a key property of privacy-respecting electronic payments, wherein the payer is not required to retain secrets arising from one transaction until the following transaction, and argue that this property is essential to framing security requirements for storage of digital assets and the risk of blackmail or coercion as a way to exfiltrate information about payment history. We claim that the design of our protocol strongly protects the anonymity of payers with respect to their payment transactions, while preventing the creation of assets by any party other than the original issuer without destroying assets of equal value.
The need for secure user authentication in blockchain-based applications has been growing with the increased adoption of Decentralized Identity (DID) credentials in blockchain.Zokrates, a tool designed to protect user privacy within smart contracts, had a limitation in that it could not accept authenticated user information such as credentials, only allowing the use of manually inputted data.In this paper, we propose a smart contract system that securely validates DID credentials to overcome the limitations of traditional centralized authentication systems.This system ensures the safe identification of users within blockchain-based applications by authenticating their identities in a trusted manner within the blockchain.As the demand for user authentication in blockchain rises, this paper emphasizes the significance of a blockchain-based identity verification system that guarantees both privacy and security.Leveraging the Zero-Knowledge Proof method and utilizing the Zokrates tool, this innovative approach aims to provide solutions for the digital identity verification process, thereby expanding the scope of blockchain technology applications.Moreover, we also provide a CLI for each entity.We help anyone who wants to authenticate their identity using the tool to safely verify it on-chain.