  Crowdsourced testing, as a distinct testing paradigm, has attracted much
attention in software testing, especially in mobile application (app) testing
field. Compared with in-house testing, crowdsourced testing shows superiority
with the diverse testing environments when faced with the mobile testing
fragmentation problem. However, crowdsourced testing also encounters the
low-quality test report problem caused by unprofessional crowdworkers involved
with different expertise. In order to handle the submitted reports of uneven
quality, app developers have to distinguish high-quality reports from
low-quality ones to help the bug inspection. One kind of typical low-quality
test report is inconsistent test reports, which means the textual descriptions
are not focusing on the attached bug-occurring screenshots. According to our
empirical survey, only 18.07% crowdsourced test reports are consistent.
Inconsistent reports cause waste on mobile app testing.
  To solve the inconsistency problem, we propose ReCoDe to detect the
consistency of crowdsourced test reports via deep image-and-text fusion
understanding. ReCoDe is a two-stage approach that first classifies the reports
based on textual descriptions into different categories according to the bug
feature. In the second stage, ReCoDe has a deep understanding of the GUI image
features of the app screenshots and then applies different strategies to handle
different types of bugs to detect the consistency of the crowdsourced test
reports. We conduct an experiment on a dataset with over 22k test reports to
evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting
the consistency of crowdsourced test reports. Besides, a user study is
conducted to prove the practical value of ReCoDe in effectively helping app
developers improve the efficiency of reviewing the crowdsourced test reports.
