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This paper introduces WebMAC, a multi-agent collaborative framework that enhances LLM-based web system scenario testing by addressing limitations in scenario description completeness and test adequacy. WebMAC employs three interacting agent modules to complete natural language descriptions, transform scenarios using equivalence class partitioning, and convert them into test scripts. Experiments across four web systems demonstrate that WebMAC significantly improves test execution success rates (30%-60%), testing efficiency (29%), and error detection capabilities, while also reducing token consumption (47.6%) compared to state-of-the-art methods.
Multi-agent collaboration and equivalence class partitioning can significantly boost the effectiveness and efficiency of LLM-driven web system testing, outperforming existing methods by a wide margin.
Scenario testing is an important technique for detecting errors in web systems. Testers draft test scenarios and convert them into test scripts for execution. Early methods relied on testers to convert test scenarios into test scripts. Recent LLM-based scenario testing methods can generate test scripts from natural language descriptions of test scenarios. However, these methods are not only limited by the incompleteness of descriptions but also overlook test adequacy criteria, making it difficult to detect potential errors. To address these limitations, this paper proposes WebMAC, a multi-agent collaborative framework for scenario testing of web systems. WebMAC can complete natural language descriptions of test scenarios through interactive clarification and transform adequate instantiated test scenarios via equivalence class partitioning. WebMAC consists of three multi-agent modules, responsible respectively for completing natural language descriptions of test scenarios, transforming test scenarios, and converting test scripts. We evaluated WebMAC on four web systems. Compared with the SOTA method, WebMAC improves the execution success rate of generated test scripts by 30%-60%, increases testing efficiency by 29%, and reduces token consumption by 47.6%. Furthermore, WebMAC can effectively detect more errors in web systems.
