Jul 2026· International Conference on Artificial Intelligence Testing· pp. 235-248· 1 citation· ⚡ 1 influential· 32 references
Abstract
Testing RESTful API is increasingly complicated but indispensable to quality assurance of cloud-native applications. This paper reports a multi-agent system called MASTEST that combines LLM-based intelligent agents and programmed agents to automate REST API testing. They form a complete tool chain covering the whole workflow of REST API test with API specification in the OpenAPI Swagger format as the input. It also incorporates human testers in the process to review and correct LLM generated test artefacts to control the quality of testing activities. MASTEST is evaluated on two LLMs, GPT-4o and DeepSeek V3.1 Reasoner with five public APIs. Its performances on various testing activities are measured by a wide range of metrics, including adequacy and coverage metrics, the syntax and data type correctness of generated test scripts, the usability of LLM generated test cases and scripts, as well as the bug detection ability. Experiment results demonstrated that both DeepSeek and GPT-4o achieved a high overall performance but had strengths and weaknesses on different testing activities. MASTEST generated test cases achieved 94% and 98% unit test coverage and 79% and 78% system test coverage for GPT-4o and DeepSeek respectively in comparison with human designed test cases. The generated test scripts maintained 100% syntax correctness and only required minimal manual edits for semantic correctness. The generated test scripts contain assertions on the expected status code as well as contents in the response messages. They are highly capable of detecting bugs in the REST APIs. Experiment data shows that the bug detection rates are between 2.13 to 4.50 per operation. These findings indicate that MASTEST is highly efficient and effective.
A multi-agent testing framework in which requirement analysis, test-case generation, sandboxed execution, and defect detection are delegated to four distinct agents, and in which detection rests on a composite rule combining the execution signal with the semantic judgment of a dedicated diagnostic agent.
Yuxuan Li· Transactions on Computing Sc...· 0 citations
TestAgent is proposed, an LLM-based test generation approach that addresses the above limitations by emulating human testing practices via a multi-agent collaboration mechanism and equips TestAgent with a set of tool APIs that can be invoked dynamically in an on-demand and adaptive manner.
Quanjun Zhang, Ye Shang, Siqi Gu et al.· arXiv.org· 0 citations
End-to-end GUI testing is essential for verifying web applications, yet existing evaluations rely on predefined checklists and are confined to the data and frameworks of web generation benchmarks, leaving the bug-discovery ability of vision-language models (VLMs) systematically untested. We introduce \textbf{C}ode-driven \textbf{A}gentic \textbf{T}esting (CAT), a paradigm in which the agent writes Playwright code to drive the browser, gathers feedback, and autonomously explores web applications to uncover bugs. We instantiate CAT with CATJudge, an agentic framework that unifies Browser-Use and Computer-Use tools within a single environment and CATTest, a benchmark of 102 AI-generated web applications with carefully annotated bugs, built through close human-AI collaboration to feature complex interactions and subtle defects. Experiments with mainstream VLMs show that all evaluated models perform poorly, revealing a clear gap between current VLM capabilities and the demands of real-world testing in AI web development. We release our code and data at https://github.com/SleepyWithoutCoffee/CATJudge.
Bin Hong, Zhen-Chao Zhang, Ji-Yuan He et al.· 0 citations
DataClawEval is introduced, the first comprehensive benchmark designed specifically to evaluate the end-to-end task completion capabilities of autonomous agents in real-world data engineering scenarios, and it comprises 100 rigorous, end-to-end tasks spanning five execution engines.
Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.
Gopalakrishnan Marimuthu· International Conference on...· 0 citations
A configurable multi-agent framework for benchmarking LLMs in Prolog code generation that combines a Code Generator Agent, a deterministic execution layer using SWI-Prolog, and an evaluator based on the LLM-as-a-Judge paradigm that supports model-agnostic experimentation and evaluates outputs across functional correctness, efficiency, structural properties and evaluator reliability.
Nikolaos Karamousalidis, P. Kefalas· SN Computer Science· 0 citations
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