# MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests

arXiv: https://arxiv.org/abs/2511.18038 · HTML: https://mlsanity.com/paper/2511.18038/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 15 of 15)

- [Leveraging Large Language Models to Improve REST API Testing](https://mlsanity.com/paper/2312.00894/) (2023)
- [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)
- [APITestGenie: Automated API Test Generation through Generative AI](https://mlsanity.com/paper/2409.03838/) (2024)
- [KAT: Dependency-aware Automated API Testing with Large Language Models](https://mlsanity.com/paper/2407.10227/) (2024)
- [LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents](https://mlsanity.com/paper/2503.15079/) (2025)
- [QuickREST: Property-based Test Generation of OpenAPI-Described RESTful APIs](https://mlsanity.com/paper/1912.09686/) (2019)
- [Automating REST API Postman Test Cases Using LLM](https://mlsanity.com/paper/2404.10678/) (2024)
- [LlamaRestTest: Effective REST API Testing with Small Language Models](https://mlsanity.com/paper/2501.08598/) (2025)
- [AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL](https://mlsanity.com/paper/2501.08600/) (2025)
- [Combining TSL and LLM to Automate REST API Testing: A Comparative Study](https://mlsanity.com/paper/2509.05540/) (2025)
- [RESTful API Automated Test Case Generation](https://mlsanity.com/paper/1901.01538/) (2019)
- [FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer](https://mlsanity.com/paper/2407.14361/) (2024)

## Abstract (arXiv)

Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system called MASTEST that combines LLM-based intelligent agents and programmed agents to automate REST API testing. They form a complete tool chain covering the whole workflow of REST API test with API specification in the OpenAPI Swagger format as the input. It also incorporates human testers in the process to review and correct LLM generated test artefacts to control the quality of testing activities. MASTEST is evaluated on two LLMs, GPT-4o and DeepSeek V3.1 Reasoner with five public APIs. Its performances on various testing activities are measured by a wide range of metrics, including adequacy and coverage metrics, the syntax and data type correctness of generated test scripts, the usability of LLM generated test cases and scripts, as well as the bug detection ability. Experiment results demonstrated that both DeepSeek and GPT-4o achieved a high overall performance but had strengths and weaknesses on different testing activities. MASTEST generated test cases achieved 94% and 98% unit test coverage and 79% and 78% system test coverage for GPT-4o and DeepSeek respectively in comparison with human designed test cases. The generated test scripts maintained 100% syntax correctness and only required minimal manual edits for semantic correctness. The generated test scripts contain assertions on the expected status code as well as contents in the response messages. They are highly capable of detecting bugs in the REST APIs. Experiment data shows that the bug detection rates are between 2.13 to 4.50 per operation. These findings indicate that MASTEST is highly efficient and effective.