AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL

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 cover...

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Veröffentlicht in:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) S. 21 - 24
Hauptverfasser: Stennett, Tyler, Kim, Myeongsoo, Sinha, Saurabh, Orso, Alessandro
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 27.04.2025
Schlagworte:
Automated REST API Testing
Codes
Fault detection
Large language models
Monitoring
Multi Agent Reinforcement Learning for Testing
Reinforcement learning
Semantics
Servers
Software engineering
Testing
Web services
ISSN:2574-1934
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Zusammenfassung: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.
ISSN:2574-1934
DOI:10.1109/ICSE-Companion66252.2025.00015