Automated, Cost-effective, and Update-driven App Testing
Apps’ pervasive role in our society led to the definition of test automation approaches to ensure their dependability. However, state-of-the-art approaches tend to generate large numbers of test inputs and are unlikely to achieve more than 50% method coverage.
In this article, we propose a strategy to achieve significantly higher coverage of the code affected by updates with a much smaller number of test inputs, thus alleviating the test oracle problem.
More specifically, we present ATUA, a model-based approach that synthesizes App models with static analysis, integrates a dynamically refined state abstraction function and combines complementary testing strategies, including (1) coverage of the model structure, (2) coverage of the App code, (3) random exploration, and (4) coverage of dependencies identified through information retrieval. Its model-based strategy enables ATUA to generate a small set of inputs that exercise only the code affected by the updates. In turn, this makes common test oracle solutions more cost-effective, as they tend to involve human effort.
A large empirical evaluation, conducted with 72 App versions belonging to nine popular Android Apps, has shown that ATUA is more effective and less effort-intensive than state-of-the-art approaches when testing App updates.
Tue 11 OctDisplayed time zone: Eastern Time (US & Canada) change
10:30 - 12:30
|Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility Issues|
Xiaoyu Sun Monash University, Xiao Chen Monash University, Yanjie Zhao Monash University, Pei Liu Monash University, John Grundy Monash University, Li Li Monash UniversityDOI Pre-print
|Automated, Cost-effective, and Update-driven App TestingVirtual|
Chanh-Duc Ngo University of Luxembourg, Fabrizio Pastore University of Luxembourg, Lionel Briand University of Luxembourg; University of OttawaLink to publication
|Fastbot2: Reusable Automated Model-based GUI Testing for Android Enhanced by Reinforcement LearningVirtual|
Vision and Emerging Results
|Right to Know, Right to Refuse: Towards UI Perception-Based Automated Fine-Grained Permission Controls for Android AppsVirtual|
Vikas K. Malviya Singapore Management University, Chee Wei Leow Singapore Management University, Ashok Kasthuri Singapore Management University, Yan Naing Tun Singapore Management University, Lwin Khin Shar Singapore Management University, Lingxiao Jiang Singapore Management UniversityPre-print Media Attached
|MalWhiteout: Reducing Label Errors in Android Malware DetectionVirtual|
|AUSERA: Automated Security Vulnerability Detection for Android AppsVirtual|
|A Comprehensive Evaluation of Android ICC Resolution TechniquesVirtual|
Jiwei Yan Institute of Software at Chinese Academy of Sciences, China, Shixin Zhang Beijing Jiaotong University, China, Yepang Liu Southern University of Science and Technology, Xi Deng Institute of Software, Chinese Academy of Sciences, Jun Yan Institute of Software at Chinese Academy of Sciences, China, Jian Zhang Institute of Software at Chinese Academy of Sciences, ChinaDOI Pre-print