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

• URL: http://arxiv.org/abs/2510.27417v1
• Date: Fri, 31 Oct 2025 12:12:01 GMT
• Title: Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications
• Authors: Jarne Besjes, Robbe Nooyens, Tolgahan Bardakci, Mutlu Beyazit, Serge Demeyer,
• Abstract summary: This study extends prior work on Large Language Model (LLM) based test amplification.<n>The amplified test suites maintain semantic validity with minimal human intervention.<n>A detailed analysis of computational cost, runtime, and energy consumption highlights trade-offs between accuracy, scalability, and efficiency.
• Score: 0.48933451909251763
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Automated structural testing of LLM-based agents: methods, framework, and case studies [0.05254956925594667]
  LLM-based agents are rapidly being adopted across diverse domains.<n>Current testing approaches focus on acceptance-level evaluation from the user's perspective.<n>We present methods to enable structural testing of LLM-based agents.
  arXiv  Detail & Related papers  (2026-01-25T11:52:30Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Combining TSL and LLM to Automate REST API Testing: A Comparative Study [3.8615905456206256]
  RestTSLLM is an approach that uses Test Specification Language (TSL) in conjunction with Large Language Models (LLMs) to automate the generation of test cases for REST APIs.<n>The proposed solution integrates prompt engineering techniques with an automated pipeline to evaluate various LLMs on their ability to generate tests from OpenAPI specifications.<n>The results indicate that the best-performing LLMs consistently produced robust and contextually coherent REST API tests.
  arXiv  Detail & Related papers  (2025-09-05T23:32:35Z)
• Evaluating LLMs on Sequential API Call Through Automated Test Generation [10.621357661774244]
  StateGen is an automated framework designed to generate diverse coding tasks involving sequential API interactions.<n>We construct StateEval, a benchmark encompassing 120 verified test cases spanning across three representative scenarios.<n> Experimental results confirm that StateGen can effectively generate challenging and realistic API-oriented tasks.
  arXiv  Detail & Related papers  (2025-07-13T03:52:51Z)
• Test Amplification for REST APIs via Single and Multi-Agent LLM Systems [1.6499388997661122]
  We investigate the use of large language model (LLM) systems, both single-agent and multi-agent setups, for amplifying existing REST API test suites.<n>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.
  arXiv  Detail & Related papers  (2025-04-10T20:19:50Z)
• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs.<n>We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool.<n>Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• The BrowserGym Ecosystem for Web Agent Research [151.90034093362343]
  BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents.<n>We propose an extended BrowserGym-based ecosystem for web agent research, which unifies existing benchmarks from the literature.<n>We conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across 6 popular web agent benchmarks.
  arXiv  Detail & Related papers  (2024-12-06T23:43:59Z)
• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• Adaptive REST API Testing with Reinforcement Learning [54.68542517176757]
  Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally. Current tools struggle when response schemas are absent in the specification or exhibit variants. We present an adaptive REST API testing technique incorporates reinforcement learning to prioritize operations during exploration.
  arXiv  Detail & Related papers  (2023-09-08T20:27:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.