RESTInfer: automated inferring parameter constraints from natural language RESTful API descriptions

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, it is difficult for automated tools to generate feasible parameters under complicating constraints randomly. Fortunately, RESTful API descriptions can be used to infer possible parameter constraints. Given parameter constraints, automated tools can further improve the efficiency of testing. In this paper, we propose RESTInfer, a two-phase approach to infer parameter constraints by natural language processing. The preliminary evaluation result shows that RESTInfer can achieve a high code coverage and bug finding.