LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Automated testing for REST APIs has become essential for ensuring the correctness and reliability of modern web services. While existing approaches primarily focus on detecting server crashes and error codes, they often overlook logical issues that arise due to evolving business logic and domain-specific requirements. To address this limitation, we propose LogiAgent, a novel approach for logical testing of REST systems. Built upon a large language model (LLM)-driven multi-agent framework, LogiAgent integrates a Test Scenario Generator, API Request Executor, and API Response Validator to collaboratively generate, execute, and validate API test scenarios. Unlike traditional testing methods that focus on status codes like 5xx, LogiAgent incorporates logical oracles that assess responses based on business logic, ensuring more comprehensive testing. The system is further enhanced by an Execution Memory component that stores historical API execution data for contextual consistency. We conduct extensive experiments across 12 real-world REST systems, demonstrating that LogiAgent effectively identifies 234 logical issues with an accuracy of 66.19%. Additionally, it basically excels in detecting server crashes and achieves superior test coverage compared to four state-of-the-art REST API testing tools. An ablation study confirms the significant contribution of LogiAgent's memory components to improving test coverage.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 11 of its 38 references that are in the map. First posted on arXiv 19 Mar 2025.
Builds on
11 of 38 references.
Closest ancestors · 1
Same direction, newest first
Other references · 10
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- 2022Automated Test Generation for REST APIs: No Time to Rest Yetcited by 13
- 2022Morest: Model-based RESTful API Testing with Execution Feedbackcited by 10
- 2023Adaptive REST API Testing with Reinforcement Learningcited by 7
- 2022Open Problems in Fuzzing RESTful APIs: A Comparison of Toolscited by 5
- 2021REST API Fuzzing by Coverage Level Guided Blackbox Testingcited by 1
Built on by
Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflowscited by 12
- 2025MASTEST: A LLM-Based Multi-Agent System For RESTful API Testscited by 0
- 2026Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Rankingcited by 0
- 2026Multi-Agent LLM-based Metamorphic Testing for REST APIscited by 0
- 2026Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actionscited by 0
- 2026Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions6 Sep 2026
- 2026Multi-Agent LLM-based Metamorphic Testing for REST APIs27 May 2026
- 2026Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking9 Mar 2026
- 2025MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests22 Nov 2025
- 2025SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows11 Aug 2025
Similar, not cited
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