LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909542661488640 |
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| author | Zhang, Ke Zhang, Chenxi Wang, Chong Zhang, Chi Wu, YaChen Xing, Zhenchang Liu, Yang Li, Qingshan Peng, Xin |
| author_facet | Zhang, Ke Zhang, Chenxi Wang, Chong Zhang, Chi Wu, YaChen Xing, Zhenchang Liu, Yang Li, Qingshan Peng, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents Zhang, Ke Zhang, Chenxi Wang, Chong Zhang, Chi Wu, YaChen Xing, Zhenchang Liu, Yang Li, Qingshan Peng, Xin Software Engineering 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. |
| title | LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.15079 |