一句话：提出 AToMIC 框架，用专用大语言模型从 JIRA 需求与代码变更自动生成 Gherkin 场景、Page Object 和可执行 UI 测试脚本，并在 BMW 应用中验证其工业可行性。
跨平台移动开发（如 Flutter）中，验收测试的编写与维护仍高度依赖人工，成为开发流程的瓶颈。本文提出 AToMIC：一个利用专用大语言模型从需求（JIRA 工单）和近期代码变更自动生成 Gherkin 场景、Page Objects 及可执行 UI 测试脚本的框架。作者在 BMW MyBMW 应用的 170 多个屏幕代码库上，针对 13 个真实 issue 进行评估。结果显示：每个功能在标准硬件上不到 5 分钟即可产出可执行测试产物；93.3% 的 Gherkin 场景生成即语法正确，78.8% 的 PageObject 无需手工修改即可运行，100% 的生成 UI 测试执行成功。实践者调查中所有参与者都报告了时间节省（常为每个功能一整个开发者日）并对采用该方案有强信心，表明 AToMIC 是工业移动项目中可扩展的实用方案。
Mobile acceptance testing remains a bottleneck in modern software development, particularly for cross-platform mobile development using frameworks like Flutter. While developers increasingly rely on automated testing tools, creating and maintaining acceptance test artifacts still demands significant manual effort. To help tackle this issue, we introduce AToMIC, an automated framework leveraging specialized Large Language Models to generate Gherkin scenarios, Page Objects, and executable UI test scripts directly from requirements (JIRA tickets) and recent code changes. Applied to BMW’s MyBMW app, covering 13 real-world issues in a 170+ screen codebase, AToMIC produced executable test artifacts in under five minutes per feature on standard hardware. The generated artifacts were of high quality: 93.3% of Gherkin scenarios were syntactically correct upon generation, 78.8% of PageObjects ran without manual edits, and 100% of generated UI tests executed successfully. In a survey, all practitioners reported time savings (often a full developer-day per feature) and strong confidence in adopting the approach. These results confirm AToMIC as a scalable, practical solution for streamlining acceptance test creation and maintenance in industrial mobile projects.
@inproceedings{paperbot9383,
title = {Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study},
author = {Pedro Luís Fonseca and Bruno Lima and João Pascoal Faria},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00273}
}