October 30, 2017

Crowd Intelligence Enhances Automated Mobile Testing

Automated Software Engineering Conference (ASE)

By: Ke Mao, Mark Harman, Yue Jia

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.