2,133 research outputs found
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DApps Ecosystems: Mapping the Network Structure of Smart Contract Interactions
Data availability - The datasets analysed during the current study can be retrieved using the tool presented in Smart contracts data of Dapps are publicly available from their respective Github repositories [https://github.com/DerwenAI/disparity_filter]. To support future work in this area, we have made our dataset publicly available via the Zenodo repository https://zenodo.org/records/12731531 and https://zenodo.org/records/13772792.Preprint available on arxiv - https://doi.org/10.48550/arXiv.2401.01991Decentralized applications (DApps) built on blockchain platforms such as Ethereum and coded in languages such as Solidity, have recently gained attention for their potential to disrupt traditional centralized systems. Despite their rapid adoption, limited research has been conducted to understand the underlying code structure of these applications. In particular, each DApp is composed of multiple smart contracts, each containing a number of functions that can be called to trigger a specific event, e.g., a token transfer. In this paper, we reconstruct and analyse the network of contracts and functions calls within the DApp, which is helpful to unveil vulnerabilities that can be exploited by malicious attackers. We show how decentralization is architecturally implemented, identifying common development patterns and anomalies that could influence the system’s robustness and efficiency. We find a consistent network structure characterized by modular, self-sufficient contracts and a complex web of function interactions, indicating common coding practices across the blockchain community. Critically, a small number of key functions within each DApp play a central role in maintaining network connectivity, making them potential targets for cyber attacks and highlighting the need for robust security measures.Ethereum foundation grant FY23-104
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Blockchain Financial Statements: Innovating Financial Reporting, Accounting, and Liquidity Management
Data Availability Statement: Data are contained within the article.The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management.This research received no external funding
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A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functions
The preprint version archived on this insttutional repository is available online at: https://arxiv.org/abs/2305.09402 . It has not been certified by peer review.This paper evaluates the capability of two state-of-the-art artificial intelligence (AI) models, GPT-3.5 and Bard, in generating Java code given a function description. We sourced the descriptions from CodingBat.com, a popular online platform that provides practice problems to learn programming. We compared the Java code generated by both models based on correctness, verified through the platform's own test cases. The results indicate clear differences in the capabilities of the two models. GPT-3.5 demonstrated superior performance, generating correct code for approximately 90.6% of the function descriptions, whereas Bard produced correct code for 53.1% of the functions. While both models exhibited strengths and weaknesses, these findings suggest potential avenues for the development and refinement of more advanced AI-assisted code generation tools. The study underlines the potential of AI in automating and supporting aspects of software development, although further research is required to fully realize this potential
Football clubs' efficiency and COVID-19 in the Big-5 European leagues
This study aims to contribute to the recent literature on the effects of COVID on football teams’
performance, focusing on the impact of playing behind closed doors – due to the health and
safety measures following the COVID-19 pandemic outbreak - on offensive and defensive
technical efficiency. Using a long season-level dataset for the top 5 European leagues, a novelty
for efficiency studies on football, the analysis compares the ten seasons (2009-10 to 2018-19)
played before the pandemic outbreak with the only season (2020-21) entirely played behind
closed doors. The methodology applied to calculate the efficiency scores is the conditional
order-m, whose application represents a further novel contribution to the literature on football
teams’ efficiency. Our findings are consistent with the recent literature on the impact of ghost
games on teams’ performance and show an erosion of the home advantage, likely due to the
reduced pressure on visiting teams deriving from the lack of home crowd support
Do developers really worry about refactoring re-test? An empirical study of open-source systems
© Springer Nature Switzerland AG 2018. In this paper, we explore the extent to which a set of over 12000 refactorings fell into one of four re-test categories defined by van Deursen and Moonen; the ‘least disruptive’ of the four categories contains refactorings requiring only minimal re-test. The ‘most disruptive’ category of refactorings on the other hand requires significant re-test effort. We used multiple versions of three open-source systems to answer one research question: Do developers prefer to undertake refactorings in the least disruptive categories or in the most disruptive? The simple answer is that they prefer to do both. We provide insights into these refactoring patterns across the systems and highlight a fundamental weakness with software metrics trying to capture the refactoring process
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