ADF-GA: Data Flow Criterion Based Test Case Generation for Ethereum Smart Contracts

TL;DR

This paper introduces ADF-GA, a novel data flow criterion-based test case generation method using genetic algorithms for Ethereum smart contracts, improving coverage and efficiency in dynamic testing.

Contribution

It presents a new approach combining data flow analysis and genetic algorithms for effective test case generation in Solidity smart contracts.

Findings

Achieves higher coverage of variable definition-use pairs.

Reduces the number of iterations needed in genetic algorithms.

Effectively generates valid test cases for Solidity programs.

Abstract

Testing is an important technique to improve the quality of Ethereum smart contract programs. However, current work on testing smart contract only focus on static problems of smart contract programs. A data flow oriented test case generation approach for dynamic testing of smart contract programs is still missing. To address this problem, this paper proposes a novel test case generation approach, called ADF-GA (All-uses Data Flow criterion based test case generation using Genetic Algorithm), for Solidity based Ethereum smart contract programs. ADF-GA aims to efficiently generate a valid set of test cases via three stages. First, the corresponding program control flow graph is constructed from the source codes. Second, the generated control flow graph is analyzed to obtain the variable information in the Solidity programs, locate the require statements, and also get the definition-use pairs to be tested. Finally, a genetic algorithm is used to generate test cases, in which an improved fitness function is proposed to calculate the definition-use pairs coverage of each test case with program instrumentation. Experimental studies are performed on several representative Solidity programs. The results show that ADF-GA can effectively generate test cases, achieve better coverage, and reduce the number of iterations in genetic algorithm.