  API testing has increasing demands for software companies. Prior API testing
tools were aware of certain types of dependencies that needed to be concise
between operations and parameters. However, their approaches, which are mostly
done manually or using heuristic-based algorithms, have limitations due to the
complexity of these dependencies. In this paper, we present KAT (Katalon API
Testing), a novel AI-driven approach that leverages the large language model
GPT in conjunction with advanced prompting techniques to autonomously generate
test cases to validate RESTful APIs. Our comprehensive strategy encompasses
various processes to construct an operation dependency graph from an OpenAPI
specification and to generate test scripts, constraint validation scripts, test
cases, and test data. Our evaluation of KAT using 12 real-world RESTful
services shows that it can improve test coverage, detect more undocumented
status codes, and reduce false positives in these services in comparison with a
state-of-the-art automated test generation tool. These results indicate the
effectiveness of using the large language model for generating test scripts and
data for API testing.
