Publication

Crowd Intelligence Enhances Automated Mobile Testing

Automated Software Engineering Conference (ASE)


Abstract

We show that information extracted from crowdbased testing can enhance automated mobile testing. We introduce POLARIZ, which generates replicable test scripts from crowd-based testing, extracting cross-app ‘motif’ events: automatically-inferred reusable higher-level event sequences composed of lower-level observed event actions. Our empirical study used 434 crowd workers from Mechanical Turk 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 crowdbased and search-based testing.

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