  Crowdtesting is effective especially when it comes to the feedback on GUI
systems, or subjective opinions about features. Despite of this, we find
crowdtesting reports are highly replicated, i.e., 82% of them are replicates of
others. Hence automatically detecting replicate reports could help reduce
triaging efforts. Most of the existing approaches mainly adopted textual
information for replicate detection, and suffered from low accuracy because of
the expression gap. Our observation on real industrial crowdtesting data found
that when dealing with crowdtesting reports of GUI systems, the reports would
accompanied with images, i.e., the screenshots of the app. We assume the
screenshot to be valuable for replicate crowdtesting report detection because
it reflects the real scenario of the failure and is not affected by the variety
of natural languages.
  In this work, we propose a replicate detection approach, TSDetector, which
combines information from the screenshots and the textual descriptions to
detect replicate crowdtesting reports. We extract four types of features to
characterize the screenshots and the textual descriptions, and design an
algorithm to detect replicates based on four similarity scores derived from the
four different features respectively. We investigate the effectiveness and
advantage of TSDetector on 15 commercial projects with 4,172 reports from one
of the Chinese largest crowdtesting platforms.Results show that TSDetector can
outperform existing state-of-the-art approaches significantly. In addition, we
also evaluate its usefulness using real-world case studies. The feedback from
real-world testers demonstrates its practical value
