  The widespread adoption of REST APIs, coupled with their growing complexity
and size, has led to the need for automated REST API testing tools. Current
testing tools focus on the structured data in REST API specifications but often
neglect valuable insights available in unstructured natural-language
descriptions in the specifications, which leads to suboptimal test coverage.
Recently, to address this gap, researchers have developed techniques that
extract rules from these human-readable descriptions and query knowledge bases
to derive meaningful input values. However, these techniques are limited in the
types of rules they can extract and can produce inaccurate results. This paper
presents RESTGPT, an innovative approach that leverages the power and intrinsic
context-awareness of Large Language Models (LLMs) to improve REST API testing.
RESTGPT takes as input an API specification, extracts machine-interpretable
rules, and generates example parameter values from natural-language
descriptions in the specification. It then augments the original specification
with these rules and values. Our preliminary evaluation suggests that RESTGPT
outperforms existing techniques in both rule extraction and value generation.
Given these encouraging results, we outline future research directions for
leveraging LLMs more broadly for improving REST API testing.

Software Engineering