mlsanityData to 14 Oct 2026

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

arXiv 2510.27417 ↗First posted 31 Oct 2025cs.SEcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

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.

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.

Builds on

21 of 49 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Test Amplification for REST APIs via Single and Multi-Agent LLM Systems10 Apr
  2. 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation14 Jan

Other references · 19

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2021Program Synthesis with Large Language Modelscited by 3,637
  4. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  5. 2023Code Llama: Open Foundation Models for Codecited by 2,139
  6. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,392
  7. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 194
  8. 2024MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notescited by 80
  9. 2024Automated Unit Test Improvement using Large Language Models at Metacited by 75
  10. 2023Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Studycited by 44
  11. 2024On the Evaluation of Large Language Models in Unit Test Generationcited by 34
  12. 2025Agentic Systems: A Guide to Transforming Industries with Vertical AI Agentscited by 20
7 more
  1. 2022Automated Test Generation for REST APIs: No Time to Rest Yetcited by 13
  2. 2024APITestGenie: Automated API Test Generation through Generative AIcited by 7
  3. 2021Restats: A Test Coverage Tool for RESTful APIscited by 4
  4. 2017A Snowballing Literature Study on Test Amplificationcited by 3
  5. 2025AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARLcited by 3
  6. 2024FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzercited by 2
  7. 2025Test Amplification for REST APIs Using "Out-of-the-box" Large Language Modelscited by 2

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