Streamlining Acceptance Test Generation for Mobile Applications Through Large Language Models: An Industrial Case Study

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.