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

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arxiv 2503.15079 v1 pith:SUF26YLJ submitted 2025-03-19 cs.SE

classification cs.SE
keywords logiagenttestinglogicalresttestsystemsautomatedbusiness
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions

    cs.SE 2026-05 unverdicted novelty 7.0 of 10

    Sakura is a multi-agent system that generates structurally complex tests from NL descriptions, achieving 50-78% higher compilability and 38-66% higher coverage overlap than baselines on 1,464 scenarios from 20 Apache ...

  2. OOPS: Automated generation of REST API specification via LLMs

    cs.SE 2026-01 conditional novelty 6.0 of 10

    OOPS uses LLM agents and an API dependency graph to generate OpenAPI specs from REST API server code across multiple languages and frameworks, with reported F1 above 92% on 12 APIs.

  3. SAINT: Service-level Integration Test Generation with Program Analysis and LLM-based Agents

    cs.SE 2025-11 conditional novelty 6.0 of 10

    SAINT automatically generates both endpoint-level and scenario-based REST API tests for enterprise Java apps using static analysis and LLM agents, outperforming EvoMaster on code coverage in several benchmarks.

  4. WFC/WFD: Web Fuzzing Commons, Dataset and Guidelines to Support Experimentation in REST API Fuzzing

    cs.SE 2025-09 conditional novelty 6.0 of 10

    The paper introduces standardized schemas for REST API authentication and fault reporting, a 36-API Docker-backed dataset, and a six-fuzzer comparison with guidelines for fair experimentation.

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