Model-based testing

Computer security

Set (abstract data type)

Blockchain

Data mining

Computer science

Fuzz testing

Code (set theory)

Distributed computing

Relation (database)

Regression analysis

Software

Random testing

Physics

Source code

Optics

Test case

Code coverage

Focus (optics)

Software engineering

Programming language

Unit testing

Integration testing

Machine learning

Blockchain-based decentralized applications (DApp) have been widely adopted in different areas and trusted by more and more users due to the fact that the back end code of a DApp is publicly run on the blockchain and cannot be modified implicitly. However, there are few effective methods and tools for testing DApps and bugs can be easily introduced by inexperienced developers. The existing testing techniques either focus on testing front-end programs or back-end code but ignore the interaction between them, which makes it difficult to apply the techniques directly on DApp. In this paper, we present an automated testing technique for DApps which works in a two-phase manner. First, we employ random events to infer an abstract relation between browser-side events and blockchain-side contracts. Second, our technique generates a set of test cases under the guidance of inferred relations and orders the test cases based on a read-write graph. We also use taint analysis to track data flow of the smart contract and feed it to the generation procedure for following test cases. We have developed a tool called Sungari to implement our approach, and evaluated it on representative real-world DApps. The preliminary evaluation results demonstrated the potential of Sungari in achieving a significant optimization compared to random testing approaches.

Towards Automated Testing of Blockchain-Based Decentralized Applications