# WebMAC: A Multi-Agent Collaborative Framework for Scenario Testing of Web Systems

arXiv: https://arxiv.org/abs/2604.13559 · HTML: https://mlsanity.com/paper/2604.13559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 15 Apr 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 14 of 14)

- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Beyond Self-Talk: A Communication-Centric Survey of LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2502.14321/) (2025)
- [Make LLM a Testing Expert: Bringing Human-like Interaction to Mobile GUI Testing via Functionality-aware Decisions](https://mlsanity.com/paper/2310.15780/) (2023)
- [Fill in the Blank: Context-aware Automated Text Input Generation for Mobile GUI Testing](https://mlsanity.com/paper/2212.04732/) (2022)
- [On the Evaluation of Large Language Models in Unit Test Generation](https://mlsanity.com/paper/2406.18181/) (2024)
- [Generating Test Scenarios from NL Requirements using Retrieval-Augmented LLMs: An Industrial Study](https://mlsanity.com/paper/2404.12772/) (2024)
- [Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain](https://mlsanity.com/paper/2309.16134/) (2023)
- [Scenario-Guided LLM-based Mobile App GUI Testing](https://mlsanity.com/paper/2506.05079/) (2025)
- [GUIPilot: A Consistency-based Mobile GUI Testing Approach for Detecting Application-specific Bugs](https://mlsanity.com/paper/2506.07385/) (2025)
- [Large Language Models for Automated Web-Form-Test Generation: An Empirical Study](https://mlsanity.com/paper/2405.09965/) (2024)
- [CrashScope: A Practical Tool for Automated Testing of Android Applications](https://mlsanity.com/paper/1801.06428/) (2018)

## Abstract (arXiv)

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.