@inproceedings{dd2dfe4ec5dd44eab63143f80b1981f7,
title = "ACAT: A novel machine-learning-based tool for automating android application testing",
abstract = "Mobile applications are being used every day by more than half of the world{\textquoteright}s population to perform a great variety of tasks. With the increasingly widespread usage of these applications, the need arises for efficient techniques to test them. Many frameworks allow automating the process of application testing, however existing frameworks mainly rely on the application developer for providing testing scripts for each developed application, thus preventing reuse of these tests for similar applications. In this demonstration, we present a novel tool for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios. We discuss and demonstrate the potential benefits of our tool in an empirical study where we show it outperforms standard methods in realistic settings.",
keywords = "Activities classification, Android application testing, Demonstration, Mobile testing automation",
author = "Ariel Rosenfeld and Odaya Kardashov and Orel Zang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 13th International Haifa Verification Conference, HVC 2017 ; Conference date: 13-11-2017 Through 15-11-2017",
year = "2017",
doi = "https://doi.org/10.1007/978-3-319-70389-3_14",
language = "الإنجليزيّة",
isbn = "9783319703886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "213--216",
editor = "Rachel Tzoref-Brill and Ofer Strichman",
booktitle = "Hardware and Software",
address = "ألمانيا",
}