TY - GEN
T1 - Random GUI testing of android application using behavioral model
AU - Muangsiri, Woramet
AU - Takada, Shingo
N1 - Funding Information:
ACKNOWLEDGEMENT This work was supported by JSPS KAKENHI JP15K00104.
PY - 2017
Y1 - 2017
N2 - Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time. In this work, we propose a behavioralbased GUI testing approach for mobile applications that achieves faster and higher coverage. Our approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model is mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. We evaluated our approach on three open-source Android applications, and compared it with other approaches. Our approach showed the effectiveness of our tool.
AB - Automated GUI testing based on behavioral model is one of the most efficient testing approaches. By mining user usage, test scenarios can be generated based on statistical models such as Markov chain. However, these works require static analysis before starting the exploration which requires too much prerequisites and time. In this work, we propose a behavioralbased GUI testing approach for mobile applications that achieves faster and higher coverage. Our approach does not conduct static analysis. It creates a behavioral model from usage logs by applying a statistical model. The events within the behavioral model is mapped to GUI components in a GUI tree. Finally, it updates the model dynamically to increase the probability of an event that rarely or never occurs when users use the application. We evaluated our approach on three open-source Android applications, and compared it with other approaches. Our approach showed the effectiveness of our tool.
KW - Android
KW - Behavioral model
KW - GUI testing
KW - Testing automation
KW - Testing tools
UR - http://www.scopus.com/inward/record.url?scp=85029515830&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029515830&partnerID=8YFLogxK
U2 - 10.18293/SEKE2017-099
DO - 10.18293/SEKE2017-099
M3 - Conference contribution
AN - SCOPUS:85029515830
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 266
EP - 271
BT - Proceedings - SEKE 2017
PB - Knowledge Systems Institute Graduate School
T2 - 29th International Conference on Software Engineering and Knowledge Engineering, SEKE 2017
Y2 - 5 July 2017 through 7 July 2017
ER -