Application of API automation testing based on microservice mode in industry software
Na Li, Jun Wang, Chen Chen, Hongfei Hu · 2024
Set against the backdrop of a corporate cloud billing system testing initiative, this paper delves into the pragmatic approach to API automation testing within a microservices architectural context. It commences by underscoring the significance and the inherent challenges posed by API testing in a microservices ecosystem, with a particular focus on the quandaries encountered when managing intricate and voluminous test data. To surmount these obstacles and enhance both the efficacy and scope of testing, the research advocates for an innovative paradigm in test data administration and procreation. This paradigm harnesses machine learning techniques to automate the generation of high-fidelity test data. By leveraging machine learning algorithms to dissect historical data and discern patterns of application utilization, the methodology affords the creation of test datasets that mirror authentic operational scenarios. Such an approach substantially elevates the pertinence and exhaustiveness of the test data, concurrently diminishing the demand for labor-intensive manual test case design. During the regression testing phase, the expounded microservices-based API automation testing strategy has demonstrated its efficacy in bolstering software quality and refining the testing process's efficiency. The paper concludes by encapsulating best practices for API automation testing within microservices architectures and suggests avenues for future research aimed at further streamlining software testing protocols and propelling ongoing advancements in industry software quality assurance.