AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL
This addresses the need for more effective testing tools for developers and testers of web services, but it appears incremental as it builds on existing methods like MARL and LLMs for a specific domain.
The authors tackled the problem of low code coverage in REST API testing due to vast search spaces and dependencies by developing AutoRestTest, a tool that integrates Semantic Property Dependency Graphs with Multi-Agent Reinforcement Learning and large language models, resulting in improved fault detection through automated generation of operation sequences and parameter combinations.
As REST APIs have become widespread in modern web services, comprehensive testing of these APIs is increasingly crucial. Because of the vast search space of operations, parameters, and parameter values, along with their dependencies and constraints, current testing tools often achieve low code coverage, resulting in suboptimal fault detection. To address this limitation, we present AutoRestTest, a novel tool that integrates the Semantic Property Dependency Graph (SPDG) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing. AutoRestTest determines operation-dependent parameters using the SPDG and employs five specialized agents (operation, parameter, value, dependency, and header) to identify dependencies of operations and generate operation sequences, parameter combinations, and values. Through an intuitive command-line interface, users can easily configure and monitor tests with successful operation count, unique server errors detected, and time elapsed. Upon completion, AutoRestTest generates a detailed report highlighting errors detected and operations exercised. In this paper, we introduce our tool and present preliminary findings, with a demonstration video available at https://www.youtube.com/watch?v=VVus2W8rap8.