Comparative analysis of Espresso and Appium frameworks for automated UI testing of Android mobile applications
Article Sidebar
Issue Vol. 40 (2026)
-
Analysis of the capabilities of predictive artificial intelligence models in corporate risk management
Kacper Ziemski188-192
-
Usability and availability of selected e-commerce services
Marcin Kozicki, Maria Skublewska-Paszkowska193-200
-
Comparison of C++ and Python performance based on selected algorithms
Szymon Bogucki, Kacper Burda201-205
-
Security analysis of selected web applications using vulnerability scanners
Mariusz Choroś, Marta Dziuba-Kozieł206-212
-
Comparison of Java and .NET reflection mechanisms for dynamic module loading: a performance benchmark study
Michał Mazur, Sebastian Maruszak, Marek Miłosz213-217
-
Comparative analysis of network vulnerability detection tools
Mateusz Zdunek218-225
-
Evaluation of mobile applications for personal finance management using the MARS scale
Łukasz Nikiel, Artsiom Patskevich, Marek Miłosz226-231
-
Comparison of the effectiveness of roulette betting strategies using Monte Carlo simulation
Marek Sarnecki232-238
-
Analysis of optimization capabilities of selected database management systems
Paweł Tarkiewicz, Małgorzata Plechawska-Wójcik239-246
-
Comparative analysis of Espresso and Appium frameworks for automated UI testing of Android mobile applications
Jakub Derkacz247-254
-
Comparative analysis of the applicability of artificial intelligence models for code generation
Patryk Warchoł, Małgorzata Plechawska-Wójcik255-262
-
Comparison of the effectiveness of selected tools for detecting texts generated by artificial intelligence
Marcin Brodacki, Małgorzata Plechawska-Wójcik263-269
-
Comparative analysis of selected containerization tools in terms of MCP
Paweł Jan Tłusty, Maciej Pańczyk270-276
-
SpikeCliff effect: empirical analysis of deterministic timing discontinuities in sponge-based XOF functions
Łukasz Wójcik, Stanisław Lota277-282
-
Comparison of AI agents for creating SQL queries
Julia Sierpień, Maria Skublewska-Paszkowska283-288
-
Comparative analysis of the performance of PostgreSQL and Neo4j databases in the context of genealogical queries
Michał Muzyka, Mateusz Niedźwiedź, Marek Miłosz289-296
-
Evaluation of the effectiveness of static and dynamic methods in malware analysis
Dominik Tracz, Daniel Sawicki, Konrad Gromaszek297-303
-
Comparison of classical machine learning methods in the task of obesity level classification
Paweł Biesaga, Paweł Powroźnik304-312
Main Article Content
Authors
Abstract
This paper presents an empirical comparison of two leading frameworks for automated UI testing of Android applications: Espresso and Appium. An identical suite of 65 test cases was implemented in both frameworks for the MediTrack medication management application and executed in ten independent runs. Analysis of stable runs showed that Espresso completed the test suite in 437.4 s on average, while Appium required 727.7 s, a slowdown factor of 1.66. Per-module ratios ranged from 0.71 to 2.78, depending on test complexity. Appium achieved higher run stability (80% vs 60%), while standard deviations of stable runs were comparable. The results provide empirical guidance for choosing between the frameworks based on project requirements.
Keywords:
Sustainable Development Goal (SDG)
- Industry, Innovation, Technology and Infrastructure
References
[1] Mobile Operating System Market Share Worldwide, StatCounter, https://gs.statcounter.com/os-market-share/mobile/worldwide, [20.04.2026].
[2] IEEE, IEEE Standard for Software and System Test Documentation, IEEE Std 829-2008, IEEE, 2008, https://doi.org/10.1109/IEEESTD.2008.4578383.
[3] G. J. Myers, C. Sandler, T. Badgett, The Art of Software Testing, 3rd ed., John Wiley & Sons, Hoboken, 2012, https://doi.org/10.1002/9781119202486.
[4] J. Humble, D. Farley, Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation, Addison-Wesley, Boston, 2010.
[5] M. Cohn, Succeeding with Agile: Software Development Using Scrum, Addison-Wesley, Upper Saddle River, 2010.
[6] Test Pyramid, M. Fowler, https://martinfowler.com/bliki/TestPyramid.html, [23.04.2026].
[7] S. Braun, F. Elberzhager, K. Holl, Automation support for mobile app quality assurance – a tool landscape, Procedia Computer Science 110 (2017) 117–124, https://doi.org/10.1016/j.procs.2017.06.129.
[8] Espresso — Android Developers, Google LLC, https://developer.android.com/training/testing/espresso, [23.04.2026].
[9] WebDriver — W3C Recommendation, W3C, https://www.w3.org/TR/webdriver/, [12.03.2026].
[10] J. Wang, J. Wu, Research on mobile application automation testing technology based on Appium, In 2019 International Conference on Virtual Reality and Intelligent Systems (ICVRIS) (2019) 247–250, https://doi.org/10.1109/ICVRIS.2019.00068.
[11] G. da Silva, R. de Souza Santos, Comparing mobile testing tools using documentary analysis, In ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (2023) 1–6, https://doi.org/10.1109/ESEM56168.2023.10304798.
[12] D. Zelenchuk, Android Espresso Revealed: Writing Automated UI Tests, Apress, New York, 2019.
[13] R. C. Martin, Clean Architecture: A Craftsman's Guide to Software Structure and Design, Prentice Hall, 2017.
[14] K. S. Arif, U. Ali, Mobile application testing tools and their challenges: A comparative study, In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (2019) 1–6, https://doi.org/10.1109/ICOMET.2019.8673505.
[15] R. K. Lenka, S. Mamgain, S. Kumar, R. K. Barik, Performance analysis of automated testing tools: JMeter and TestComplete, In 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (2018) 399–407, https://doi.org/10.1109/ICACCCN.2018.8748521.
[16] R. Gu, J. M. Rojas, An Empirical Study on the Adoption of Scripted GUI Testing for Android Apps, Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering Workshops (2023), https://doi.org/10.1109/ASEW60602.2023.00030.
[17] R. Coppola, M. Morisio, M. Torchiano, Maintenance of Android widget-based GUI testing: A taxonomy of test case modification causes, In 2018 IEEE International Conference on Software Testing, Verification and Validation Workshops (2018) 151–158, https://doi.org/10.1109/ICSTW.2018.00044.
[18] S. K. Purushothaman, N. Kashyap, S. Agarwal, S. Iqbal, V. Behere, Unified approach towards automation of any desktop web, mobile web, Android, iOS, REST and SOAP API use cases, In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) (2018) 1–5 , https://doi.org/10.1109/ICCSDET.2018.8821229.
[19] R. Coppola, L. Ardito, M. Torchiano, E. Alegroth, Translation from layout-based to visual Android test scripts: An empirical evaluation, Journal of Systems and Software 171 (2021) 110845, https://doi.org/10.1016/j.jss.2020.110845.
[20] G. Lovreto, A. T. Endo, P. Nardi, V. H. S. Durelli, Automated tests for mobile games: An experience report, In 2018 17th Brazilian Symposium on Computer Games and Digital Entertainment (2018) 73–82, https://doi.org/10.1109/SBGAMES.2018.00015.
[21] D. Vajak, R. Grbić, M. Vranješ, D. Stefanović, Environment for automated functional testing of mobile applications, In 2018 International Conference on Smart Systems and Technologies (SST) (2018) 125–130, https://doi.org/10.1109/SST.2018.8564626.
[22] S. Mojahed, R. Drouin, L. Sboui, ODACE: An Appium-based testing automation platform for Android mobile devices certification, In 2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (2024) 301–308, https://doi.org/10.1109/ICSTW60967.2024.00060.
[23] I. Arcuschin, L. Di Meo, M. Auer, J. P. Galeotti, G. Fraser, Brewing up reliability: Espresso test generation for Android apps, In 2024 IEEE Conference on Software Testing, Verification and Validation (ICST) (2024) 185–196, https://doi.org/10.1109/ICST60714.2024.00025.
[24] S. Negara, N. Esfahani, R. Buse, Practical Android test recording with Espresso Test Recorder, In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (2019) 193–202, https://doi.org/10.1109/ICSE-SEIP.2019.00029.
[25] C. Q. Adamsen, G. Mezzetti, A. Møller, Systematic execution of Android test suites in adverse conditions, Proceedings of the 2015 International Symposium on Software Testing and Analysis (2015) 83–93, https://doi.org/10.1145/2771783.2771786.
[26] Q. Luo, F. Hariri, L. Eloussi, D. Marinov, An empirical analysis of flaky tests, Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering (2014) 643–653, https://doi.org/10.1145/2635868.2635920.
[27] Y. D. Lin, J. F. Rojas, E. T. H. Chu, Y. C. Lai, On the accuracy, efficiency, and reusability of automated test oracles for Android devices, IEEE Transactions on Software Engineering 40 (2014) 957–970, https://doi.org/10.1109/TSE.2014.2331982.
[28] P. Tramontana, D. Amalfitano, N. Amatucci, A. R. Fasolino, Automated functional testing of mobile applications: a systematic mapping study, Software Quality Journal 27(1) (2019) 149–201, https://doi.org/10.1007/s11219-018-9418-6.
[29] S. Di Martino, A. R. Fasolino, L. L. L. Starace, P. Tramontana, Comparing the effectiveness of capture and replay against automatic input generation for Android graphical user interface testing, Software Testing, Verification and Reliability 31(3) (2021) e1754, https://doi.org/10.1002/stvr.1754.
[30] Firebase Local Emulator Suite, Google LLC, https://firebase.google.com/docs/emulator-suite, [15.03.2026].
Article Details
Abstract views: 2

