Smart-Graph: Graphical Representations for Smart Contract on the Ethereum Blockchain

Abstract

The Ethereum blockchain enables executing and recording smart contracts. The smart contracts can facilitate, verify, and implement the negotiation between multiple parties, also guaranteeing transactions without a traditional legal entity. Many tools supporting the smart contracts development in different areas are flourishing because in Ethereum blockchain valuable assets are often involved. Some of the tools help the developer to find security vulnerabilities via static and/or dynamic analysis or to reduce the Gas fees consumption. Despite the plethora of such tools, there is no tool supporting smart contracts evaluation and analysis via a graphical representation for expert developers.The paper embraces this way to facilitate the developers' analysis activity, by proposing a graphical representation model to visualize smart contract source code. The paper makes available a tool via a web interface, which accepts the smart contract address as an input and produces a graphical representation of the smart contract as an output. The graphical representation can help developers to better understand the structure of smart contracts and share it with other developers. Moreover, some metrics, such as the relations among smart contracts, are easier to be understood via "spatial" than "tabular" representation. Indeed, representing smart contracts' metrics via visual representation facilitates the developers, who are used to analyze the source code by directly inspecting it or using other tools that provide the metrics in a table format. Finally, the paper provides detailed data regarding a smart contract to the developers and proposes a graphical representation of the smart contracts without obscuration of details, also highlighting areas of the code that are possibly too big in size and/or too complex via a diagram displaying their connections.

Code Will Tell: Visual Identification of Ponzi Schemes on Ethereum

“…Wen et al [30] proposed NFTDisk, a visual analytic system, to help investors detect wash trading in NFT markets. Also, a few visualization approaches have also been proposed to analyze the source code of smart contracts for different purposes such as facilitating smart contract development [26] and Solidity code representation [22]. However, none of the above studies has attempted to visualize the source code of smart contracts for Ponzi scheme detection on Ethereum, which is the focus of this paper.…”

Solidity Code Generation From UML State Machines in Model-Driven Smart Contract Development

“…In addition to the proposed structured approaches, numerous less broad proposals exist that do not cover the entirety of SDLC but try to incorporate different existing notations for modeling the smart contract structure and behavior. The proposals encompass the usage of UML class diagrams [31], [32], UML sequence diagrams [29], UML state diagrams [20], UML deployment diagram [33], finite state machines (FSM) [28], BPMN [30], [34], [35], and even custom domain specific languages (DSL) [36]- [38] in smart contract development. In our research, we focus on transformation from models to smart contract code, and for this reason, we have analyzed in more detail several of the aforementioned proposals [30], [34], [35], [37], [39] that place a heavier focus on the usage of models for smart contract code generation purposes.…”

Smart Contracts Security Threats and Solutions

“…Several approaches are then proposed to ease smart contract development: Logic-based languages (Prolog) (Zibin et al, 2019;Florian et al, 2016), IELE (Scillia, Yul) (Tyurin & al., 2019), Use of type based-language Idris, Simplicity (O'Connor R., 2017), liquidity (Çagdas & al., 2018), Obsidian (Coblenz, 2017), Flint (Schrans & al., 2018), Mandala (Markus, 2019) SmaCoNat (Regnath & Steinhors ., 2018), Bitml (Tyurin & al., 2019), SPESC (Xiao & al., 2018), iContract (Qasse & al., 20201), Smart-Graph (Pierro., 2021), and SuMo (Barboni & al., 2021).…”