通过深入理解屏幕截图优先处理众包测试报告
Prioritize Crowdsourced Test Reports via Deep Screenshot Understanding
摘要：
众包测试在移动应用程序（app）测试中越来越占主导地位，但对app开发人员来说，检查数量惊人的测试报告是一个巨大的负担。许多研究已经被提出来处理仅基于文本或另外简单的图像特征的测试报告。然而，在移动应用程序测试中，测试报告中包含的文本被压缩，信息不足。许多屏幕截图作为补充包括在内，包含文本之外的更丰富的信息。这种趋势促使我们根据对屏幕截图的深入理解来优先考虑众包测试报告。在本文中，我们提出了一种新的众包测试报告优先级排序方法，即DeepPrior。我们首先用一个新引入的特性来表示众包测试报告，即DeepFeature，它包括所有小部件及其文本、坐标、类型，甚至是基于对应用程序截图的深入分析的意图，以及众包测试报告中的文本描述。DeepFeature包括Bug Feature和Context Feature，前者直接描述Bug，后者描述Bug的完整上下文。DeepFeature的相似度用于表示测试报告的相似度，并对众包测试报告进行优先级排序。我们将相似性正式定义为DeepSimilarity。我们还进行了实证实验，以评估所提出的技术与一个大的数据集组的有效性。结果表明，DeepPrior很有前途，它比最先进的方法性能更好，开销不到一半。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
