KAT: Dependency-aware Automated API Testing with Large Language Models

TL;DR

KAT uses large language models and advanced prompting to autonomously generate comprehensive API test cases, improving coverage and accuracy over existing tools.

Contribution

This paper introduces KAT, a novel AI-driven API testing approach leveraging GPT and dependency graph construction from OpenAPI specifications.

Findings

Improves test coverage of RESTful APIs

Detects more undocumented status codes

Reduces false positives in testing results

Abstract

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.