ACAT: A novel machine-learning-based tool for automating android application testing

Ariel Rosenfeld; Odaya Kardashov; Orel Zang

doi:10.1007/978-3-319-70389-3_14

ACAT: A novel machine-learning-based tool for automating android application testing

Ariel Rosenfeld, Odaya Kardashov, Orel Zang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Mobile applications are being used every day by more than half of the world’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.

Original language	English
Title of host publication	Hardware and Software
Subtitle of host publication	Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings
Editors	Rachel Tzoref-Brill, Ofer Strichman
Publisher	Springer Verlag
Pages	213-216
Number of pages	4
ISBN (Print)	9783319703886
DOIs	https://doi.org/10.1007/978-3-319-70389-3_14
State	Published - 2017
Externally published	Yes
Event	13th International Haifa Verification Conference, HVC 2017 - Haifa, Israel Duration: 13 Nov 2017 → 15 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10629 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Haifa Verification Conference, HVC 2017
Country/Territory	Israel
City	Haifa
Period	13/11/17 → 15/11/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Activities classification
Android application testing
Demonstration
Mobile testing automation

Access to Document

10.1007/978-3-319-70389-3_14

Cite this

Rosenfeld, A., Kardashov, O., & Zang, O. (2017). ACAT: A novel machine-learning-based tool for automating android application testing. In R. Tzoref-Brill, & O. Strichman (Eds.), Hardware and Software: Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings (pp. 213-216). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10629 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70389-3_14

Rosenfeld, Ariel ; Kardashov, Odaya ; Zang, Orel. / ACAT : A novel machine-learning-based tool for automating android application testing. Hardware and Software: Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings. editor / Rachel Tzoref-Brill ; Ofer Strichman. Springer Verlag, 2017. pp. 213-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Rosenfeld, A, Kardashov, O & Zang, O 2017, ACAT: A novel machine-learning-based tool for automating android application testing. in R Tzoref-Brill & O Strichman (eds), Hardware and Software: Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10629 LNCS, Springer Verlag, pp. 213-216, 13th International Haifa Verification Conference, HVC 2017, Haifa, Israel, 13/11/17. https://doi.org/10.1007/978-3-319-70389-3_14

ACAT: A novel machine-learning-based tool for automating android application testing. / Rosenfeld, Ariel; Kardashov, Odaya; Zang, Orel.
Hardware and Software: Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings. ed. / Rachel Tzoref-Brill; Ofer Strichman. Springer Verlag, 2017. p. 213-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10629 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Mobile applications are being used every day by more than half of the world’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.

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Rosenfeld A, Kardashov O, Zang O. ACAT: A novel machine-learning-based tool for automating android application testing. In Tzoref-Brill R, Strichman O, editors, Hardware and Software: Verification and Testing - 13th International Haifa Verification Conference, HVC 2017, Proceedings. Springer Verlag. 2017. p. 213-216. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-70389-3_14

ACAT: A novel machine-learning-based tool for automating android application testing

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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Cite this