Proposal of an automated testing method for GraphQL APIs using reinforcement learning

Abstract

GraphQL is an API query language, and because it has a different structure from REST APIs, it is difficult to apply conventional automated testing methods, so a new approach is required. This research proposes an automated testing method for GraphQL APIs using reinforcement learning. In the proposed method, the test space is explored using Q-learning. A request is generated by selecting API fields and arguments based on the schema, and the Q-value is updated according to the response. By repeating this process and learning, efficient black-box testing is achieved. In the experiment, the effectiveness of the proposed method was verified using schema coverage and the rate of error responses as evaluation indicators for publicly available APIs. In the future, we plan to improve the Q-value initialization and reward design to avoid local optimum solutions, and further confirm the effectiveness through comparison with other methods.