# Automated Test Input Generation for Android: Are We There Yet?

arXiv: https://arxiv.org/abs/1503.07217 · HTML: https://mlsanity.com/paper/1503.07217/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2015.

## Built on by (most cited first, 10 of 10)

- [Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning](https://mlsanity.com/paper/2309.01519/) (2023)
- [Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applications](https://mlsanity.com/paper/2103.07297/) (2021)
- [Android Security using NLP Techniques: A Review](https://mlsanity.com/paper/2107.03072/) (2021)
- [ROI: A method for identifying organizations receiving personal data](https://mlsanity.com/paper/2204.09495/) (2022)
- [Genetic Micro-Programs for Automated Software Testing with Large Path Coverage](https://mlsanity.com/paper/2302.07646/) (2023)
- [AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding](https://mlsanity.com/paper/2404.01240/) (2024)
- [CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation](https://mlsanity.com/paper/2601.21253/) (2026)
- [GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing](https://mlsanity.com/paper/2605.29532/) (2026)
- [An Empirical Study for Android-to-OpenHarmony GUI Test Migration](https://mlsanity.com/paper/2607.11245/) (2026)
- [Can LLMs Test Terminal User Interfaces?](https://mlsanity.com/paper/2608.03743/) (2026)

## Abstract (arXiv)

Mobile applications, often simply called "apps", are increasingly widespread, and we use them daily to perform a number of activities. Like all software, apps must be adequately tested to gain confidence that they behave correctly. Therefore, in recent years, researchers and practitioners alike have begun to investigate ways to automate apps testing. In particular, because of Android's open source nature and its large share of the market, a great deal of research has been performed on input generation techniques for apps that run on the Android operating systems. At this point in time, there are in fact a number of such techniques in the literature, which differ in the way they generate inputs, the strategy they use to explore the behavior of the app under test, and the specific heuristics they use. To better understand the strengths and weaknesses of these existing approaches, and get general insight on ways they could be made more effective, in this paper we perform a thorough comparison of the main existing test input generation tools for Android. In our comparison, we evaluate the effectiveness of these tools, and their corresponding techniques, according to four metrics: code coverage, ability to detect faults, ability to work on multiple platforms, and ease of use. Our results provide a clear picture of the state of the art in input generation for Android apps and identify future research directions that, if suitably investigated, could lead to more effective and efficient testing tools for Android.