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

arXiv: https://arxiv.org/abs/2501.08600 · HTML: https://mlsanity.com/paper/2501.08600/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 15 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Value-Decomposition Networks For Cooperative Multi-Agent Learning](https://mlsanity.com/paper/1706.05296/) (2017)
- [Leveraging Large Language Models to Improve REST API Testing](https://mlsanity.com/paper/2312.00894/) (2023)
- [Automated Test Generation for REST APIs: No Time to Rest Yet](https://mlsanity.com/paper/2204.08348/) (2022)
- [Morest: Model-based RESTful API Testing with Execution Feedback](https://mlsanity.com/paper/2204.12148/) (2022)
- [Testing RESTful APIs: A Survey](https://mlsanity.com/paper/2212.14604/) (2022)
- [A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs](https://mlsanity.com/paper/2411.07098/) (2024)
- [DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2408.08594/) (2024)
- [Adaptive REST API Testing with Reinforcement Learning](https://mlsanity.com/paper/2309.04583/) (2023)

## Built on by (most cited first, 3 of 3)

- [Test Amplification for REST APIs via Single and Multi-Agent LLM Systems](https://mlsanity.com/paper/2504.08113/) (2025)
- [Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications](https://mlsanity.com/paper/2510.27417/) (2025)
- [MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests](https://mlsanity.com/paper/2511.18038/) (2025)

## Abstract (arXiv)

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.