Abstract With the widespread application of smart contracts, there is a growing concern over the quality assurance of smart contracts. The data flow testing is an important technology to ensure the correctness of smart contracts. We propose an approach named IABC‐TCG (Improved Artificial Bee Colony‐Test Case Generation) to generate test cases for the data flow testing of smart contracts. With a dominance relations‐based fitness function, an improved artificial bee colony algorithm is used to generate test cases, in which the bee colony search coefficient is adaptively adjusted to improve the effectiveness and efficiency of the search. In addition, an improved test case selection and updation strategy is used to avoid unnecessary test cases. The experimental results show that IABC‐TCG achieves 100% coverage for all the test requirements on a dataset of 30 smart contracts and outperforms the baseline approaches in terms of the number of test cases and the execution time. Performing tests with the generated test cases, IABC‐TCG can find more errors with less test cost.

Smart contracts are commonly deployed for safety-critical applications, the quality assurance of which has been a vital factor. Test cases are standard means to ensure the correctness of data flows in smart contracts. To more efficiently generate test cases with high coverage, we propose an improved genetic algorithm-based test-case generation approach for smart contract data flow testing. Our approach introduces the theory of particle swarm optimization into the genetic algorithm, which reduces the influence brought by the randomness of genetic operations and enhances its capability to find global optima. A set of 30 real smart contracts deployed on Ethereum and GitHub is collected to perform the experimental study, on which our approach is compared with three baseline approaches. The experimental results show that, in most cases, the coverage of the test cases generated by our approach is significantly higher than the baseline approaches with relatively lower numbers of iterations and lower execution time.

With the widespread use of smart contracts, security incidents caused by improper programming have drawn increasing attention. Data flow correctness is a fundamental and vital requirement for smart contracts. Although test cases generated by existing approaches for data flow testing of smart contracts can achieve certain coverage, not all def-use (definitionuse) pairs are covered, which may result in some errors not being detected. To further improve the adequacy and efficiency of testing, we present an approach named TCG-Re (T est C ase G eneration Combined with Data Flow R eduction) to generate test cases for data flow testing of smart contracts. Data flow reduction is performed to reduce redundant def-use pairs and obtain the final test requirements in a smart contract. The test case generation process is optimized to improve the coverage of the generated test cases for the def-use pairs. We collect 30 smart contracts of different scales to perform experimental evaluation. The result shows that, compared with other existing approaches, TCG-Re can not only achieve full coverage for most contracts, but also be more efficient. In addition, TCG-Re is also effective for discovering several types of errors in smart contracts.