LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2503.15079
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.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.15079
- https://arxiv.org/pdf/2503.15079
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414902662
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414902662Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2503.15079Digital Object Identifier
- Title
-
LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-AgentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-19Full publication date if available
- Authors
-
Ke Zhang, Chenxi Zhang, Chong Wang, Chi Zhang, Yadi Wu, Z. Z. Xing, Liu Yang, Qingshan Li, Xin PengList of authors in order
- Landing page
-
https://arxiv.org/abs/2503.15079Publisher landing page
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-
https://arxiv.org/pdf/2503.15079Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2503.15079Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
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