RESTful APIs are arguably the most popular endpoints for accessing Web services. Blackbox testing is one of the emerging techniques for ensuring the reliability of RESTful APIs. The major challenge in testing RESTful APIs is the need for correct sequences of API operation calls for in-depth testing. To build meaningful operation call sequences, researchers have proposed techniques to learn and utilize the API dependencies based on OpenAPI specifications. However, these techniques either lack the overall awareness of how all the APIs are connected or the flexibility of adaptively fixing the learned knowledge.

A fuzzy logic expert system for diagnosis and security assessment of power transformers (FDSAPT) is presented. A prototype of this expert-system has been accomplished with the AI-language Turbo-Prolog on a microcomputer and satisfactory application results were obtained. In building a sophisticated expert system for FD-SAPT, the main difficulties arise from the inexactness or impreciseness of the fault information, the uncertainty of the inference rules, the complexity of representing experts' knowledge, and the inference process. To solve these problems, the test information was represented by fuzzy sets obtained by the operation of predicate rules. A production rule with a certainty factor was adopted to represent the knowledge of experts, and a fuzzy inference approach based on fuzzy synthetic judgment theory was introduced into the inexact reasoning of the expert system. A complex inference mechanism with depth-first search strategy was successfully used in this system. The FDSAPT system has been applied to Beijing electric power system and satisfactory results were obtained.