Test Amplification for REST APIs via Single and Multi-Agent LLM Systems
- URL: http://arxiv.org/abs/2504.08113v1
- Date: Thu, 10 Apr 2025 20:19:50 GMT
- Title: Test Amplification for REST APIs via Single and Multi-Agent LLM Systems
- Authors: Robbe Nooyens, Tolgahan Bardakci, Mutlu Beyazit, Serge Demeyer,
- Abstract summary: We show how single-agent and multi-agent LLM systems can amplify a REST API test suite.<n>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.
- Score: 1.6499388997661122
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: REST APIs (Representational State Transfer Application Programming Interfaces) are essential to modern cloud-native applications. Strong and automated test cases are crucial to expose lurking bugs in the API. However, creating automated tests for REST APIs is difficult, and it requires test cases that explore the protocol's boundary conditions. In this paper, we investigate how single-agent and multi-agent LLM (Large Language Model) systems can amplify a REST API test suite. 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.
Related papers
- Test Amplification for REST APIs Using "Out-of-the-box" Large Language Models [1.8024397171920885]
We report our experience with usingChatGPT and GitHub's Copilot to amplify REST API test suites.<n>We derive a series of guidelines and lessons learned concerning the prompts that result in the strongest test suite.
arXiv Detail & Related papers (2025-03-13T12:30:14Z) - Utilizing API Response for Test Refinement [2.8002188463519944]
This paper proposes a dynamic test refinement approach that leverages the response message.<n>Using an intelligent agent, the approach adds constraints to the API specification that are further used to generate a test scenario.<n>The proposed approach led to a decrease in the number of 4xx responses, taking a step closer to generating more realistic test cases.
arXiv Detail & Related papers (2025-01-30T05:26:32Z) - AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL [46.65963514391019]
AutoRestTest is a novel tool that integrates the Semantic Property Dependency Graph (SPDG) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing.
arXiv Detail & Related papers (2025-01-15T05:54:33Z) - 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) - ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration [70.26807758443675]
ExploraCoder is a training-free framework that empowers large language models to invoke unseen APIs in code solution.
We show that ExploraCoder significantly improves performance for models lacking prior API knowledge, achieving an absolute increase of 11.24% over niave RAG approaches and 14.07% over pretraining methods in pass@10.
arXiv Detail & Related papers (2024-12-06T19:00:15Z) - A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs [46.65963514391019]
We present AutoRestTest, the first black-box tool to adopt a dependency-embedded multi-agent approach for REST API testing.<n>Our approach treats REST API testing as a separable problem, where four agents collaborate to optimize API exploration.<n>Our evaluation of AutoRestTest on 12 real-world REST services shows that it outperforms the four leading black-box REST API testing tools.
arXiv Detail & Related papers (2024-11-11T16:20:27Z) - DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning [5.756036843502232]
This paper introduces DeepREST, a novel black-box approach for automatically testing REST APIs.
It leverages deep reinforcement learning to uncover implicit API constraints, that is, constraints hidden from API documentation.
Our empirical validation suggests that the proposed approach is very effective in achieving high test coverage and fault detection.
arXiv Detail & Related papers (2024-08-16T08:03:55Z) - KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs.
Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
arXiv Detail & Related papers (2024-07-14T14:48:18Z) - Leveraging Large Language Models to Improve REST API Testing [51.284096009803406]
RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification.
Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation.
arXiv Detail & Related papers (2023-12-01T19:53:23Z) - 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.