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

TL;DR

This paper presents AToMIC, an automated framework using Large Language Models to generate acceptance testing artifacts for mobile apps, significantly reducing manual effort and increasing efficiency in industrial settings.

Contribution

We introduce AToMIC, a novel LLM-based framework that automates acceptance test generation from requirements and code changes for mobile applications.

Findings

93.3% of Gherkin scenarios were syntactically correct

78.8% of PageObjects ran without manual edits

100% of generated UI tests executed successfully

Abstract

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.