# How do Developers Test Android Applications?

arXiv: https://arxiv.org/abs/1801.06268 · HTML: https://mlsanity.com/paper/1801.06268/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [AXNav: Replaying Accessibility Tests from Natural Language](https://mlsanity.com/paper/2310.02424/) (2023)
- [Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI Testing](https://mlsanity.com/paper/2311.08649/) (2023)
- [Agent for User: Testing Multi-User Interactive Features in TikTok](https://mlsanity.com/paper/2504.15474/) (2025)
- [GUI Test Migration via Abstraction and Concretization](https://mlsanity.com/paper/2409.05028/) (2024)
- [MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs](https://mlsanity.com/paper/2403.13690/) (2024)

## Abstract (arXiv)

Enabling fully automated testing of mobile applications has recently become an important topic of study for both researchers and practitioners. A plethora of tools and approaches have been proposed to aid mobile developers both by augmenting manual testing practices and by automating various parts of the testing process. However, current approaches for automated testing fall short in convincing developers about their benefits, leading to a majority of mobile testing being performed manually. With the goal of helping researchers and practitioners - who design approaches supporting mobile testing - to understand developer's needs, we analyzed survey responses from 102 open source contributors to Android projects about their practices when performing testing. The survey focused on questions regarding practices and preferences of developers/testers in-the-wild for (i) designing and generating test cases, (ii) automated testing practices, and (iii) perceptions of quality metrics such as code coverage for determining test quality. Analyzing the information gleaned from this survey, we compile a body of knowledge to help guide researchers and professionals toward tailoring new automated testing approaches to the need of a diverse set of open source developers.