Targeted and depth-first exploration for systematic testing of android apps

By Tanzirul Azim, Iulian Neamtiu. Conference on Object-Oriented Programming Systems, Languages, and Applications, 2013

486 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/afc5b6d4b844003810fd8aaeb171f45918bfd546

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Suman Nath
2. Koushik Sen
3. George C. Necula
4. Wontae Choi
5. Sakir Sezer
6. Yue Jia
7. Mark Harman
8. Ke Mao

In Software

1. Yue Jia
2. Koushik Sen
3. Mark Harman
4. Ke Mao
5. George C. Necula
6. Wontae Choi
7. Zhendong Su
8. Yuting Chen

In Signal Processing

1. Mark Harman
2. Yue Jia
3. Koushik Sen
4. Lorenzo Cavallaro
5. Ke Mao
6. George C. Necula
7. Wontae Choi
8. Yuting Chen

Citations in the corpus

• Guided, stochastic model-based GUI testing of Android apps (2017, methodology): “Moneky, A3E, and Sapienz are the state-of-the-art GUI testing tools.”
• Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “One can use any automated GUI testing technique, such as A3E [13], Dynodroid [69], AppsPlayground [93], or MobiGUITAR [8], to generate initial test suites.”
• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “As Android apps are highly interactive, many behaviors need to be triggered via the interface, received intents, or with smart, automatic event injectors [Azim and Neamtiu 2013; Machiry et al. 2013; Mahmood et al. 2014].”
• Automated Test Input Generation for Android: Are We There Yet? (E) (2015, unknown): “A 3 E -Targeted [5] provides an alternative exploration strategy that complements the one described in Section 3.2.”
• Static analysis of android apps: A systematic literature review (2017, background): “Approach Open-source Evaluation tool-support lab wild # of apps A3E [57] 3 3 25 A5 [59] 3 3 1260 Adagio [65] 3 3 147950 Amandroid [68] 3 3 3 853 Anadroid [69] 3 0 Androguard2 [74] 3 3 0 Androguard [76] 3 3 0 android-app-analysis-tool [78] 3 3 265 ApkCombiner [141] 3 3 3000 Apparecium [83] 3 3 100 As”
• Sapienz: multi-objective automated testing for Android applications (2016, methodology): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”
• DL-Droid: Deep learning based android malware detection using real devices (2019, methodology): “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”
• PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, background): “AE [8] executes and visits app pages to uncover potential bugs.”
• Brahmastra: Driving Apps to Test the Security of Third-Party Components (2014, methodology): “A3E [14] also uses static analysis to find an activity transition graph and uses the graph to efficiently explore apps.”
• 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.”