mlsanityData to 7 Oct 2026

LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents

arXiv 2503.15079 ↗First posted 19 Mar 2025cs.SEcited by 5

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

  1. 2024A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs11 Nov

Other references · 10

  1. 2022Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?cited by 1,413
  2. 2022A Survey on In-context Learningcited by 717
  3. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  4. 2023Software Testing with Large Language Models: Survey, Landscape, and Visioncited by 163
  5. 2023Leveraging Large Language Models to Improve REST API Testingcited by 20
  6. 2022Automated Test Generation for REST APIs: No Time to Rest Yetcited by 13
  7. 2022Morest: Model-based RESTful API Testing with Execution Feedbackcited by 10
  8. 2023Adaptive REST API Testing with Reinforcement Learningcited by 7
  9. 2022Open Problems in Fuzzing RESTful APIs: A Comparison of Toolscited by 5
  10. 2021REST API Fuzzing by Coverage Level Guided Blackbox Testingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Test Amplification for REST APIs via Single and Multi-Agent LLM Systems
  2. 2025A Framework for Testing and Adapting REST APIs as LLM Tools
  3. 2025Combining TSL and LLM to Automate REST API Testing: A Comparative Study
  4. 2026RESTOR: Automated Test Oracle Generation for RESTful APIs via Reinforcement Learning ↗
  5. 2026REST API Testing with Verified LLM-Inferred Dependencies and Response-Driven Refinement ↗
  6. 2025Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications
  7. 2026VibeCheck: Assessing the Quality of LLM-Generated Unit Tests: A Multi-agent Empirical Study across Heterogeneous Repositories
  8. 2026Automated structural testing of LLM-based agents: methods, framework, and case studies