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.

GUI testing has been prevailing in software testing. However, existing automated GUI testing tools mostly rely on frameworks of a specific platform. Testers have to fully understand platform features before developing platform-dependent GUI testing tools. Starting from the perspective of tester’s vision, we observe that GUIs on different platforms share commonalities of widget images and layout designs, which can be leveraged to achieve platform-independent testing. We propose UniRLTest, an automated software testing framework, to achieve platform independence testing. UniRLTest utilizes computer vision techniques to capture all the widgets in the screenshot and constructs a widget tree for each page. A set of all the executable actions in each tree will be generated accordingly. UniRLTest adopts a Deep Q-Network, a reinforcement learning (RL) method, to the exploration process and formalize the Android GUI testing problem to a Marcov Decision Process (MDP), where RL could work. We have conducted evaluation experiments on 25 applications from different platforms. The result shows that UniRLTest outperforms baselines in terms of efficiency and effectiveness.

The fragmentation problem has extended from Android to different platforms, such as iOS, mobile web, and even mini-programs within some applications (app), like WeChat. In such a situation, recording and replaying test scripts is one of the most popular automated mobile app testing approaches. However, such approach encounters severe problems when crossing platforms. Different versions of the same app need to be developed to support different platforms relying on different platform supports. Therefore, mobile app developers need to develop and maintain test scripts for multiple platforms aimed at completely the same test requirements, greatly increasing testing costs. However, we discover that developers adopt highly similar user interface layouts for versions of the same app on different platforms. Such a phenomenon inspires us to replay test scripts from the perspective of similar UI layouts. In this paper, we propose an image-driven mobile app testing framework, utilizing Widget Feature Matching and Layout Characterization Matching to analyze app UIs. We use computer vision (CV) technologies to perform UI feature comparison and layout hierarchy extraction on mobile app screenshots to obtain UI structures containing rich contextual information of app widgets, including coordinates, relative relationship, etc. Based on acquired UI structures, we can form a platform-independent test script, and then locate the target widgets under test. Thus, the proposed framework non-intrusively replays test scripts according to a novel platform-independent test script model. We also design and implement a tool named LIRAT to devote the proposed framework into practice, based on which, we conduct an empirical study to evaluate the effectiveness and usability of the proposed testing framework. The results show that the overall replay accuracy reaches around 65.85% on Android (8.74% improvement over state-of-the-art approaches) and 35.26% on iOS (35% improvement over state-of-the-art approaches).

Crowdsourced mobile testing has been widely used due to its convenience and high efficiency [10]. Crowdsourced workers complete testing tasks and record results in test reports. However, the problem of duplicate reports has prevented the efficiency of crowdsourced mobile testing from further improving. Existing crowdsourced testing report analysis techniques usually leverage screenshots and text descriptions independently, but fail to recognize the link between these two types of information. In this paper, we present a crowdsourced mobile testing report selection tool, namely STIFA, to extract image and text feature information in reports and establish an image-text-fusion bug context. Based on text and image fusion analysis results, STIFA performs cluster analysis and report selection. To evaluate, we employed STIFA to analyze 150 reports from 2 apps. The results show that STIFA can extract, on average, 95.23% text feature information and 84.15% image feature information. Besides, STIFA reaches an accuracy of 87.64% in detecting duplicate reports. The demo can be found at https://youtu.be/Gw6ptqyQbQY.