# Multi-Agent LLM-based Metamorphic Testing for REST APIs

arXiv: https://arxiv.org/abs/2605.28321 · HTML: https://mlsanity.com/paper/2605.28321/

## 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 10 of its 34 references that are in the map.
- First posted on arXiv 27 May 2026.

## Placed in

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

## Builds on (most cited first, 10 of 10)

- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Metamorphic Testing: A New Approach for Generating Next Test Cases](https://mlsanity.com/paper/2002.12543/) (2020)
- [Automated Test Generation for REST APIs: No Time to Rest Yet](https://mlsanity.com/paper/2204.08348/) (2022)
- [Testing RESTful APIs: A Survey](https://mlsanity.com/paper/2212.14604/) (2022)
- [Towards Generating Executable Metamorphic Relations Using Large Language Models](https://mlsanity.com/paper/2401.17019/) (2024)
- [Can ChatGPT advance software testing intelligence? An experience report on metamorphic testing](https://mlsanity.com/paper/2310.19204/) (2023)
- [LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents](https://mlsanity.com/paper/2503.15079/) (2025)
- [Deriving Semantics-Aware Fuzzers from Web API Schemas](https://mlsanity.com/paper/2112.10328/) (2021)
- [AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2510.19438/) (2025)
- [RESTifAI: LLM-Based Workflow for Reusable REST API Testing](https://mlsanity.com/paper/2512.08706/) (2025)

## Abstract (arXiv)

As REST APIs become an increasingly significant part of software systems, their validation is becoming more critical. Hence, testing and uncovering underlying issues are of utmost importance for improving software quality. However, testing REST APIs is challenging mainly due to the difficulty of assessing whether the output of an API call is correct, i.e., the test oracle problem. Metamorphic testing is a specification-based testing approach for situations where correct outputs are unknown or not specified explicitly. To check the correctness of a system, relations between the different outputs are specified. We present ARMeta, a tool-supported approach that uses an LLM-based multi-agent workflow to support metamorphic testing of REST APIs documented with OpenAPI. The agentic workflow is used to identify metamorphic test scenarios and specify them in the Given-When-Then format. These scenarios are automatically implemented as executable tests and executed against the system under test. We evaluate ARMeta on two publicly available web applications that expose REST interfaces and compare its performance with a scenario-based testing baseline. The results show that ARMeta explores behaviors that serve as a complement to existing scenario-based testing approaches.