Guided GUI testing of android apps with minimal restart and approximate learning

By Wontae Choi, George C. Necula, Koushik Sen. Conference on Object-Oriented Programming Systems, Languages, and Applications, 2013

391 citations recorded by the source corpus.

Subfields: Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eef49388eadde17a4c450023f821f8bf5ebb352c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Software

1. Yue Jia
2. Ke Mao
3. Ting Su
4. Yang Liu
5. Zhendong Su
6. Mark Harman
7. Yuting Chen
8. Yao Guo

In Signal Processing

1. Ting Su
2. Yuting Chen
3. Zhendong Su
4. Ke Mao
5. Yue Jia
6. Yao Guo
7. Mark Harman
8. Sakir Sezer

Citations in the corpus

• DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Choudhary et al. (2015) demonstrated that among the input generation tools analysed comparatively in their study (i.e. Monkey Developers (2012) , Dynodroid Machiry et al. (2013) , ACTEve Anand et al. (2012) , A3E Azim and Neamtiu (2013) , GUIRip-per Amalfitano et al. (2012) , SwiftHand Choi et al. (2”
• Automated Test Input Generation for Android: Are We There Yet? (E) (2015, neutral): “Some tools (PUMA and SwiftHand) are compatible only with the most recent releases of the Android Framework, while others (ACTEve and Dynodroid) are bound to a very old one.”
• Sapienz: multi-objective automated testing for Android applications (2016, methodology): “SwiftHand [27] dynamically builds a finite state machine model of the GUI, seeking to reduce restart time, while improving test coverage.”
• Guided, stochastic model-based GUI testing of Android apps (2017, methodology): “Stoat isnotcomparedwithothermodel-based tools because they are either unavailable ( e.g. , ORBIT [67] and AMOLA [31]) or crash frequently ( e.g. , Swifthand [15]).”
• EvoDroid: segmented evolutionary testing of Android apps (2014, neutral): “Choi et al. [25] proposed a machine learning approach to improve the app models by exploring the states not encountered during manual testing.”
• DroidBot: A Lightweight UI-Guided Test Input Generator for Android (2017, methodology): “AndroidRipper [7], SwiftHand [9], A3E [13] and GUICC [14] are model-based automated test generators, while using different methods to construct the model and generate input based on the model.”