TY - GEN
T1 - Automation of Android Applications Functional Testing Using Machine Learning Activities Classification
AU - Rosenfeld, Ariel
AU - Kardashov, Odaya
AU - Zang, Orel
N1 - Publisher Copyright: © 2018 ACM.
PY - 2018/5/27
Y1 - 2018/5/27
N2 - Following the ever-growing demand for mobile applications, researchers are constantly developing new test automation solutions for mobile developers. However, researchers have yet to produce an automated functional testing approach, resulting in many developers relying on a resource consuming manual testing. In this paper, we present a novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques and reusing generic test scenarios. Our approach aims at relieving some of the manual functional testing burden by automatically classifying each of the application's screens to a set of common screen behaviors for which generic test scripts can be instantiated and reused. We empirically demonstrate the potential benefits of our approach in two experiments: First, using 26 randomly selected Android applications, we show that our approach can successfully instantiate and reuse generic functional tests and discover functional bugs. Second, in a human study with two experienced human mobile testers, we show that our approach can automatically cover a large portion of the human testers' work suggesting a significant potential relief in the manual testing efforts.
AB - Following the ever-growing demand for mobile applications, researchers are constantly developing new test automation solutions for mobile developers. However, researchers have yet to produce an automated functional testing approach, resulting in many developers relying on a resource consuming manual testing. In this paper, we present a novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques and reusing generic test scenarios. Our approach aims at relieving some of the manual functional testing burden by automatically classifying each of the application's screens to a set of common screen behaviors for which generic test scripts can be instantiated and reused. We empirically demonstrate the potential benefits of our approach in two experiments: First, using 26 randomly selected Android applications, we show that our approach can successfully instantiate and reuse generic functional tests and discover functional bugs. Second, in a human study with two experienced human mobile testers, we show that our approach can automatically cover a large portion of the human testers' work suggesting a significant potential relief in the manual testing efforts.
KW - Activities Classification
KW - Android Applications Testing
KW - Mobile Testing Automation
UR - http://www.scopus.com/inward/record.url?scp=85051650921&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3197231.3197241
DO - https://doi.org/10.1145/3197231.3197241
M3 - منشور من مؤتمر
SN - 9781450357128
T3 - Proceedings - International Conference on Software Engineering
SP - 122
EP - 132
BT - 2018 IEEE/ACM 5TH INTERNATIONAL CONFERENCE ON MOBILE SOFTWARE ENGINEERING AND SYSTEMS (MOBILESOFT)
T2 - 5th ACM/IEEE International Conference on Mobile Software Engineering and Systems (MOBILESoft)
Y2 - 27 May 2018 through 28 May 2018
ER -