Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding

Crowdsourced testing is increasingly dominant in mobile application (app) testing, hut 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 its 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 tests, 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 Rug Feature, which directly describes the bugs. and the Context Feature, which depicts the thorough context of the hug. 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 DEEPFEATURE. 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.

Automated summary

Crowdsourced testing is increasingly popular in mobile app testing, but it poses a challenge for developers to manage the large number of test reports. This study introduces a novel approach called DEEPPRIOR that prioritizes crowdsourced test reports based on deep screenshot understanding. By representing test reports using a new feature called DEEPFEATURE, which includes widget details and bug context, the technique has shown promising results in outperforming existing methods with less overhead.