Automated Test Input Generation for Android: Are We There Yet?
Not on the map. Why?
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 4.
10 papers.
- 2023Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learningcited by 2
- 2021Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applicationscited by 0
- 2021Android Security using NLP Techniques: A Reviewcited by 0
- 2022ROI: A method for identifying organizations receiving personal datacited by 0
- 2023Genetic Micro-Programs for Automated Software Testing with Large Path Coveragecited by 0
- 2024AURORA: Navigating UI Tarpits via Automated Neural Screen Understandingcited by 0
- 2026CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentationcited by 0
- 2026GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testingcited by 0
- 2026An Empirical Study for Android-to-OpenHarmony GUI Test Migrationcited by 0
- 2026Can LLMs Test Terminal User Interfaces?cited by 0
- 2026Can LLMs Test Terminal User Interfaces?4 Aug 2026
- 2026An Empirical Study for Android-to-OpenHarmony GUI Test Migration13 Jul 2026
- 2026GUITestScape: Towards Open-set Evaluation on Exploratory GUI Testing28 May 2026
- 2026CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation29 Jan 2026
- 2024AURORA: Navigating UI Tarpits via Automated Neural Screen Understanding1 Apr 2024
- 2023Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning4 Sep 2023
- 2023Genetic Micro-Programs for Automated Software Testing with Large Path Coverage14 Feb 2023
- 2022ROI: A method for identifying organizations receiving personal data19 Apr 2022
- 2021Android Security using NLP Techniques: A Review7 Jul 2021
- 2021Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applications12 Mar 2021