Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding

TL;DR

This paper introduces DeepPrior, a novel approach for prioritizing crowdsourced mobile app test reports by deeply analyzing screenshots to extract rich widget and bug information, outperforming existing methods.

Contribution

DeepPrior leverages deep screenshot analysis to create comprehensive features for test report prioritization, significantly improving effectiveness and efficiency over prior text-based or simple image feature methods.

Findings

DeepPrior outperforms state-of-the-art approaches in test report prioritization.

DeepFeature effectively captures widget and bug information from screenshots.

The approach reduces overhead by more than half compared to existing methods.

Abstract

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.