# Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications

arXiv: https://arxiv.org/abs/2510.27417 · HTML: https://mlsanity.com/paper/2510.27417/

## 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 21 of its 49 references that are in the map.
- First posted on arXiv 31 Oct 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, 21 of 21)

- [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)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (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)
- [MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes](https://mlsanity.com/paper/2412.19260/) (2024)
- [Automated Unit Test Improvement using Large Language Models at Meta](https://mlsanity.com/paper/2402.09171/) (2024)
- [Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study](https://mlsanity.com/paper/2309.08221/) (2023)
- [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)
- [FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer](https://mlsanity.com/paper/2407.14361/) (2024)
- [Test Amplification for REST APIs Using "Out-of-the-box" Large Language Models](https://mlsanity.com/paper/2503.10306/) (2025)
- [Test Amplification for REST APIs via Single and Multi-Agent LLM Systems](https://mlsanity.com/paper/2504.08113/) (2025)

## Abstract (arXiv)

Representational State Transfer (REST) APIs are a cornerstone of modern cloud native systems. Ensuring their reliability demands automated test suites that exercise diverse and boundary level behaviors. Nevertheless, designing such test cases remains a challenging and resource intensive endeavor. This study extends prior work on Large Language Model (LLM) based test amplification by evaluating single agent and multi agent configurations across four additional cloud applications. The amplified test suites maintain semantic validity with minimal human intervention. The results demonstrate that agentic LLM systems can effectively generalize across heterogeneous API architectures, increasing endpoint and parameter coverage while revealing defects. Moreover, a detailed analysis of computational cost, runtime, and energy consumption highlights trade-offs between accuracy, scalability, and efficiency. These findings underscore the potential of LLM driven test amplification to advance the automation and sustainability of REST API testing in complex cloud environments.