基于深度图文融合理解的移动应用众包测试报告一致性检测
Mobile App Crowdsourced Test Report Consistency Detection via Deep Image-and-Text Fusion Understanding
AI总结：
针对移动应用众包测试中报告图文不一致的问题，提出两阶段方法ReCoDe，通过深度图文融合理解检测报告一致性，在超2.2万份报告的数据集上验证有效，可提升开发者审核效率。
摘要：
众包测试作为一种独特的测试范式，在软件测试尤其是移动应用（app）测试领域受到广泛关注。与内部测试相比，面对移动测试的碎片化问题，众包测试凭借多样化的测试环境展现出优势。然而，由于参与的众包工作者专业水平参差不齐，众包测试也面临着测试报告质量低下的问题。为了处理提交的质量不均的报告，应用开发者必须区分高质量和低质量报告，以辅助缺陷检查。一类典型的低质量测试报告是不一致报告，即文本描述与所附的缺陷发生截图不匹配。根据我们的实证调查，仅18.07%的众包测试报告是一致的。不一致报告会造成移动应用测试的资源浪费。\n为解决不一致问题，我们提出ReCoDe方法，通过深度图文融合理解检测众包测试报告的一致性。ReCoDe是一种两阶段方法：第一阶段根据缺陷特征，基于文本描述将报告划分为不同类别；第二阶段，ReCoDe对应用截图的GUI图像特征进行深度理解，随后针对不同类型的缺陷采用不同策略，以检测众包测试报告的一致性。我们在包含超过2.2万份测试报告的数据集上开展实验评估ReCoDe，结果表明ReCoDe在检测众包测试报告一致性方面具备有效性。此外，我们还开展了用户研究，证明ReCoDe在有效帮助应用开发者提升众包测试报告审核效率方面具有实用价值。
英文摘要：
Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowdsourced testing shows superiority with the diverse testing environments when faced with the mobile testing fragmentation problem. However, crowdsourced testing also encounters the low-quality test report problem caused by unprofessional crowdworkers involved with different expertise. In order to handle the submitted reports of uneven quality, app developers have to distinguish high-quality reports from low-quality ones to help the bug inspection. One kind of typical low-quality test report is inconsistent test reports, which means the textual descriptions are not focusing on the attached bug-occurring screenshots. According to our empirical survey, only 18.07% crowdsourced test reports are consistent. Inconsistent reports cause waste on mobile app testing. To solve the inconsistency problem, we propose ReCoDe to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. ReCoDe is a two-stage approach that first classifies the reports based on textual descriptions into different categories according to the bug feature. In the second stage, ReCoDe has a deep understanding of the GUI image features of the app screenshots and then applies different strategies to handle different types of bugs to detect the consistency of the crowdsourced test reports. We conduct an experiment on a dataset with over 22k test reports to evaluate ReCoDe, and the results show the effectiveness of ReCoDe in detecting the consistency of crowdsourced test reports. Besides, a user study is conducted to prove the practical value of ReCoDe in effectively helping app developers improve the efficiency of reviewing the crowdsourced test reports.
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。
