Test Case Generation for Ethereum Smart Contract based on Data Dependency Analysis of State Variable

Jinhu Du, Song Huang, Xingya Wang, Changyou Zheng, Jin-lei Sun
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Abstract

An Ethereum smart contract is an agreement reached by multiple parties, which is guaranteed by blockchain technology to be executed in accordance with the terms expressed in the form of code. Its security needs are particularly prominent due to a large number of digital assets under management. Testing is an effective way to find flaws that threaten the security of smart contracts. However, current smart contract test case generation methods do not regard the impact of other functions in the smart contract on state variables, resulting in the inaccessibility of the control statements related to state variables and low branch coverage of the function under test. To alleviate this problem, this paper proposes SV-Gen. SV-Gen generates test cases for smart contracts through two steps: static analysis and dynamic search. In the first step, SV-Gen considers the read-write relationship between functions and state variables in the smart contract to generate a function invocation sequence for the function to be tested through a backtracking algorithm on state variables. Then the arguments of transactions to invoke each function in the sequence are generated through regex matching to form the primitive test case. In the second step, the primitive test cases constitute an initial population, and a genetic algorithm undertakes the task of evolving them to high branch coverage. The experimental results on one of the VeriSmart datasets show that SV-Gen can effectively enter the control constraints related to state variables and improve the branch coverage of smart contracts.
基于状态变量数据依赖分析的以太坊智能合约测试用例生成
以太坊智能合约是多方达成的协议,由区块链技术保证按照以代码形式表达的条款执行。由于管理着大量的数字资产,其安全需求尤为突出。测试是发现威胁智能合约安全的缺陷的有效方法。然而,目前的智能合约测试用例生成方法没有考虑智能合约中其他功能对状态变量的影响,导致状态变量相关的控制语句不可访问,被测功能分支覆盖率低。为了缓解这一问题,本文提出了SV-Gen。SV-Gen通过静态分析和动态搜索两步生成智能合约的测试用例。第一步,SV-Gen考虑智能合约中函数与状态变量之间的读写关系,通过状态变量回溯算法生成待测试函数的函数调用序列。然后通过正则表达式匹配生成调用序列中每个函数的事务参数,形成基本测试用例。在第二步中,原始测试用例构成初始种群,遗传算法承担将它们进化到高分支覆盖率的任务。在一个VeriSmart数据集上的实验结果表明,SV-Gen可以有效地输入与状态变量相关的控制约束,提高智能合约的分支覆盖率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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