Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

Fernández, Justino; Gordillo, Pablo; Román-Díez, Guillermo

doi:10.1109/access.2021.3057565

Cited by 14 publications

(6 citation statements)

References 31 publications

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“…5.4.9 Gas-related Properties. Gas-related properties address its consumption [5,18,55,80,148,171]. The following instances are addressed in the literature:…”

Section: Explicit Access Control Modifiers Usagementioning

confidence: 99%

“…A Boundedness of Loops: In a loop's termination condition, an unchecked input from a user or a comparison of integers of different sizes can cause the loop to exceed the block gas limit or be too expensive to complete and result in transaction failures with non-terminating loops or unbounded mass operations errors [55,80,131,146].…”

Section: Explicit Access Control Modifiers Usagementioning

confidence: 99%

See 1 more Smart Citation

Pre-deployment Analysis of Smart Contracts -- A Survey

Munir¹,

Taha²

2023

Preprint

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Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in significant losses. Furthermore, by design, smart contract transactions are irreversible. This creates a need for methods to ensure the correctness and security of contracts pre-deployment. Recently there has been substantial research into such methods. The sheer volume of this research makes articulating state-of-the-art a substantial undertaking. To address this challenge, we present a systematic review of the literature. A key feature of our presentation is to factor out the relationship between vulnerabilities and methods through properties. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. The methods considered include static analysis as well as dynamic analysis methods and machine learning algorithms that analyze smart contracts before deployment.Several patterns about the strengths of different methods emerge through this classification process.CCS Concepts: • General and reference → Surveys and overviews; • Software and its engineering → Automated static analysis; Dynamic analysis; Software verification.

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“…5.4.9 Gas-related Properties. Gas-related properties address its consumption [5,18,55,80,148,171]. The following instances are addressed in the literature:…”

Section: Explicit Access Control Modifiers Usagementioning

confidence: 99%

Section: Explicit Access Control Modifiers Usagementioning

confidence: 99%

Pre-deployment Analysis of Smart Contracts -- A Survey

Munir¹,

Taha²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This will enhance the buyer experience and full history of the conveyances can be tracked [33]. Few startup companies commenced working to engender a blockchain predicated solution for the automobile industry [34][35][36][37][38][39]. Hybrid blockchain can be utilized for tracking the counterfeit medicines in a pharma supply chain [40].…”

Section: Grwoth In Millions Yearmentioning

confidence: 99%

Implementation of Hybrid Blockchain in a Pre-Owned Electric Vehicle Supply Chain

Subramanian

Thampy

2021

IEEE Access

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Ecumenical carbon dioxide (CO2) emission emanates from car, bus, taxi, and motorcycle is around 45%.Electric vehicle could be the major contributor to abbreviate the pollution level in a transportation sector. According to the Forbes report the ecumenical passenger electric vehicle demand expected to reach 15% in 2025, and 23% in 2030. This research provides a blockchain predicated solution for a pre-owned electric vehicle market that could engender a trust, transparency, immutable records, and an efficient way to track the entire life cycle of a futuristic electric vehicle supply chain. At present most of the pre-owned electric conveyance purchase transpires through the third-party accommodations, websites, and mobile applications. This does not provide the authentic information on electric conveyance history, charging capabilities of the battery, history of the charging records, and performance of the driver (wear and tear affect battery life). Our solution implemented utilizing hybrid blockchain technology. Each stakeholder assigned with the Ethereum blockchain address to track the vehicle records as a distributed ledger. This design considered electric vehicle manufacturer, charging station, battery manufacturer, road convey ascendancy as major stakeholders. Astute contract designed with solidity programming. Truffle platform used to deploy keenly intellective contract in private blockchain that ascertain the privacy of electric conveyance owners and other stakeholders. Meta mask a mobile application utilized kovan network for tracking vehicle in a public blockchain. This application tested with 0.1 Ether for each transaction. Blockchain Electric Vehicle Cloud of Things (BEVCoT) concept proposed to integrate the IoT-Blockchain application in a cloud environment.

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“…Regarding security, Correas et al [19] presented a static profiling technique for Ethereum smart contracts that generate upper-bound expressions that can be used to produce profiling information. Alkhalifah et al [8] discussed the root cause of Ethereum reentrancy attacks and proposed a solution which can detect, prevent, and identify the account address of an attacker during the execution of a smart contract.…”

Section: Related Work a Blockchain Dependabilitymentioning

confidence: 99%

Chaos Engineering of Ethereum Blockchain Clients

Zhang¹,

Ron²,

Baudry³

et al. 2021

Preprint

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The Ethereum blockchain is the operational backbone of major decentralized finance platforms. As such, it is expected to be exceptionally reliable. In this paper, we present CHAOSETH, a chaos engineering tool for resilience assessment of Ethereum clients. CHAOSETH operates in the following manner: First, it monitors Ethereum clients to determine their normal behavior. Then, it injects system call invocation errors into the Ethereum clients and observes the resulting behavior under perturbation. Finally, CHAOSETH compares the behavior recorded before, during, and after perturbation to assess the impact of the injected system call invocation errors. The experiments are performed on the two most popular Ethereum client implementations: GoEthereum and OpenEthereum. We experiment with 22 different types of system call invocation errors. We assess their impact on the Ethereum clients with respect to 15 applicationlevel metrics. Our results reveal a broad spectrum of resilience characteristics of Ethereum clients in the presence of system call invocation errors, ranging from direct crashes to full resilience. The experiments clearly demonstrate the feasibility of applying chaos engineering principles to blockchains.

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Static Profiling and Optimization of Ethereum Smart Contracts Using Resource Analysis

Cited by 14 publications

References 31 publications

Pre-deployment Analysis of Smart Contracts -- A Survey

Pre-deployment Analysis of Smart Contracts -- A Survey

Implementation of Hybrid Blockchain in a Pre-Owned Electric Vehicle Supply Chain

Chaos Engineering of Ethereum Blockchain Clients

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