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.

Related Publications

All Publications

USENIX FAST - February 23, 2021

Facebook’s Tectonic Filesystem: Efficiency from Exascale

Satadru Pan, Theano Stavrinos, Yunqiao Zhang, Atul Sikaria, Pavel Zakharov, Abhinav Sharma, Shiva Shankar, Mike Shuey, Richard Wareing, Monika Gangapuram, Guanglei Cao, Christian Preseau, Pratap Singh, Kestutis Patiejunas, JR Tipton, Ethan Katz-Bassett, Wyatt Lloyd

MLSys - March 1, 2020

Predictive Precompute with Recurrent Neural Networks

Hanson Wang, Zehui Wang, Yuanyuan Ma

ACM SIGCOMM - October 26, 2020

Zero Downtime Release: Disruption-free Load Balancing of a Multi-Billion User Website

Usama Naseer, Luca Niccolini, Udip Pant, Alan Frindell, Ranjeeth Dasineni, Theophilus A. Benson

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy