# Test Amplification for REST APIs via Single and Multi-Agent LLM Systems

arXiv: https://arxiv.org/abs/2504.08113 · HTML: https://mlsanity.com/paper/2504.08113/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 10 Apr 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, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation](https://mlsanity.com/paper/2302.06527/) (2023)
- [Automated Unit Test Improvement using Large Language Models at Meta](https://mlsanity.com/paper/2402.09171/) (2024)
- [On the Evaluation of Large Language Models in Unit Test Generation](https://mlsanity.com/paper/2406.18181/) (2024)
- [CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation](https://mlsanity.com/paper/2501.07811/) (2025)
- [Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents](https://mlsanity.com/paper/2501.00881/) (2025)
- [Automated Test Generation for REST APIs: No Time to Rest Yet](https://mlsanity.com/paper/2204.08348/) (2022)
- [APITestGenie: Automated API Test Generation through Generative AI](https://mlsanity.com/paper/2409.03838/) (2024)
- [Restats: A Test Coverage Tool for RESTful APIs](https://mlsanity.com/paper/2108.08209/) (2021)
- [A Snowballing Literature Study on Test Amplification](https://mlsanity.com/paper/1705.10692/) (2017)
- [AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL](https://mlsanity.com/paper/2501.08600/) (2025)
- [Test Amplification for REST APIs Using "Out-of-the-box" Large Language Models](https://mlsanity.com/paper/2503.10306/) (2025)
- [AmPyfier: Test Amplification in Python](https://mlsanity.com/paper/2112.11155/) (2021)

## Built on by (most cited first, 1 of 1)

- [Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications](https://mlsanity.com/paper/2510.27417/) (2025)

## Abstract (arXiv)

REST APIs (Representational State Transfer Application Programming Interfaces) play a vital role in modern cloud-native applications. As these APIs grow in complexity and scale, ensuring their correctness and robustness becomes increasingly important. Automated testing is essential for identifying hidden bugs, particularly those that appear in edge cases or under unexpected inputs. However, creating comprehensive and effective test suites for REST APIs is challenging and often demands significant effort. In this paper, we investigate the use of large language model (LLM) systems, both single-agent and multi-agent setups, for amplifying existing REST API test suites. These systems generate additional test cases that aim to push the boundaries of the API, uncovering behaviors that might otherwise go untested. We present a comparative evaluation of the two approaches across several dimensions, including test coverage, bug detection effectiveness, and practical considerations such as computational cost and energy usage. Our evaluation demonstrates increased API coverage, identification of numerous bugs in the API under test, and insights into the computational cost and energy consumption of both approaches.