Random GUI Testing of Android Application Using Behavioral Model

Woramet Muangsiri, Shingo Takada

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed 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 are 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. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to find errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.

Original languageEnglish
Pages (from-to)1603-1612
Number of pages10
JournalInternational Journal of Software Engineering and Knowledge Engineering
Volume27
Issue number9-10
DOIs
Publication statusPublished - 2017 Dec 1

Fingerprint

Graphical user interfaces
Testing
Static analysis
Markov processes
Statistical Models

Keywords

  • android
  • behavioral model
  • GUI testing
  • testing automation
  • Testing tools

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence

Cite this

Random GUI Testing of Android Application Using Behavioral Model. / Muangsiri, Woramet; Takada, Shingo.

In: International Journal of Software Engineering and Knowledge Engineering, Vol. 27, No. 9-10, 01.12.2017, p. 1603-1612.

Research output: Contribution to journalArticle

@article{c3bc6092c6664abba45e667bbeaefe3d,
title = "Random GUI Testing of Android Application Using Behavioral Model",
abstract = "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. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed 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 are 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. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to find errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.",
keywords = "android, behavioral model, GUI testing, testing automation, Testing tools",
author = "Woramet Muangsiri and Shingo Takada",
year = "2017",
month = "12",
day = "1",
doi = "10.1142/S0218194017400149",
language = "English",
volume = "27",
pages = "1603--1612",
journal = "International Journal of Software Engineering and Knowledge Engineering",
issn = "0218-1940",
publisher = "World Scientific Publishing Co. Pte Ltd",
number = "9-10",

}

TY - JOUR

T1 - Random GUI Testing of Android Application Using Behavioral Model

AU - Muangsiri, Woramet

AU - Takada, Shingo

PY - 2017/12/1

Y1 - 2017/12/1

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. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed 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 are 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. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to find errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.

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. To address these challenges, we propose a behavioral-based GUI testing approach for mobile applications that achieves faster and higher coverage. The proposed 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 are 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. The proposed approach was evaluated on four open-source Android applications, and compared with the state-of-the-art tools and manual testing. The main evaluation criteria are code coverage and ability to find errors. The proposed approach performed better than the current state-of-the-art automated testing tools in most aspects.

KW - android

KW - behavioral model

KW - GUI testing

KW - testing automation

KW - Testing tools

UR - http://www.scopus.com/inward/record.url?scp=85041190538&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041190538&partnerID=8YFLogxK

U2 - 10.1142/S0218194017400149

DO - 10.1142/S0218194017400149

M3 - Article

AN - SCOPUS:85041190538

VL - 27

SP - 1603

EP - 1612

JO - International Journal of Software Engineering and Knowledge Engineering

JF - International Journal of Software Engineering and Knowledge Engineering

SN - 0218-1940

IS - 9-10

ER -