ASE 2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE 2017), October 30 – November 3, 2017, Urbana-Champaign, IL, USA

Desktop Layout

Test Generation
Technical Research
Crowd Intelligence Enhances Automated Mobile Testing
Ke Mao, Mark Harman, and Yue Jia
(University College London, UK; Facebook, UK)
Abstract: We show that information extracted from crowd-based testing can enhance automated mobile testing. We introduce Polariz, which generates replicable test scripts from crowd-based testing, extracting cross-app äóÖmotifäó» events: automatically inferred reusable higher-level event sequences composed of lower-level observed event actions. Our empirical study used 434 crowd workers from 24 countries to perform 1,350 testing tasks on 9 popular Google Play apps, each with at least 1 million user installs. The findings reveal that the crowd was able to achieve 60.5% unique activity coverage and proved to be complementary to automated search-based testing in 5 out of the 9 subjects studied. Our leave-one-out evaluation demonstrates that coverage attainment can be improved (6 out of 9 cases, with no disimprovement on the remaining 3) by combining crowd-based and search-based testing.

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Time stamp: 2019-09-21T20:30:02+02:00