Many big companies are providing cloud services through RESTful APIs nowadays. With the growing popularity of RESTful API, testing RESTful API becomes crucial. To address this issue, researchers have proposed several automatic RESTful API testing techniques. In Huawei, we design and implement an automatic RESTful API testing framework named MOREST. MOREST has been used to test ten RESTful API services and helped to detected 83 previously unknown bugs which were all confirmed and fixed by the developers. On one hand, we find that MOREST shows great capability of detecting bugs in RESTful APIs. On the other hand, we also notice that human effort is inevitable and important when applying automatic RESTful API techniques in practice.

In this paper, we focus on discussing the industry practice of using automatic RESTful API testing techniques. Specifically, we emphasize the impact of human-in-the-loop for RESTful API testing. By analyzing the human factors, we summarize insights for improving the coordination between automated tools and human experts, increasing the level of automation for the tools as well as boosting the overall testing performance.

YI LIU

Nanyang Technological University

Yuekang Li

Nanyang Technological University

Yang Liu

Nanyang Technological University

Ruiyuan Wan

Runchao Wu

Huawei Inc.

Qingkun Liu

Huawei Cloud Computing Technologies Co., Ltd

RESTful APIs have been applied to provide cloud services by various notable companies. The quality assurance of RESTful API is critical. Several automatic RESTful API testing techniques have been proposed to tame this issue. By analyzing crashes caused by each test case, developers can find potential bugs in cloud services. However, automatic tools usually generate a massive number of failed test cases. Since it is labor-intensive for developers to validate each test case, automated crash clustering is one promising solution to help debug cloud services.

In this paper, we propose RESTCluster, a two-phase crash clustering approach. The preliminary evaluation result shows that RESTCluster can achieve 100% precision in different sizes of subjects with a high recall.

YI LIU

Nanyang Technological University