  Crowdsourced testing is increasingly dominant in mobile application (app)
testing, but it is a great burden for app developers to inspect the incredible
number of test reports. Many researches have been proposed to deal with test
reports based only on texts or additionally simple image features. However, in
mobile app testing, texts contained in test reports are condensed and the
information is inadequate. Many screenshots are included as complements that
contain much richer information beyond texts. This trend motivates us to
prioritize crowdsourced test reports based on a deep screenshot understanding.
  In this paper, we present a novel crowdsourced test report prioritization
approach, namely DeepPrior. We first represent the crowdsourced test reports
with a novelly introduced feature, namely DeepFeature, that includes all the
widgets along with their texts, coordinates, types, and even intents based on
the deep analysis of the app screenshots, and the textual descriptions in the
crowdsourced test reports. DeepFeature includes the Bug Feature, which directly
describes the bugs, and the Context Feature, which depicts the thorough context
of the bug. The similarity of the DeepFeature is used to represent the test
reports' similarity and prioritize the crowdsourced test reports. We formally
define the similarity as DeepSimilarity. We also conduct an empirical
experiment to evaluate the effectiveness of the proposed technique with a large
dataset group. The results show that DeepPrior is promising, and it outperforms
the state-of-the-art approach with less than half the overhead.
