With the development of the mobile Internet, the quality requirements of mobile apps are getting higher and higher. The traditional industry's quality assurance method is that testers perform periodic tests during the app development process. This test method can be called active testing, that is, testing. People actively test the app.
The quality of the test depends on the comprehensiveness of the use case input, and it is reflected in the product that it contains scenarios and data.
Although the testers have covered most of the product's functions with their own testing experience and some testing theories (exploratory testing, precision testing, etc.), there are still problems such as incompleteness and lack of specificity. Some functional testers are There is no guarantee for testing. In-app tests such as video player app playback failure, file opening app opening file failure, etc. The use case input constructed by the tester has no way to completely cover the videos and files in question.
Mobile app release generally undergoes functional testing, integration testing, internal testing and several rounds of grayscale testing. The grayscale testing phase has been put in the hands of some users, and the release is finally completed. The mobile terminal facing the mobile app and the scene environment are all There are many differences, and the traditional method of constructing input use cases cannot be fully tested. Here is a test model that uses online data to improve the quality of mobile apps. The core idea of the model is to report abnormal data using buried points, and to refine cluster analysis. Data analysis models such as association analysis extract targeted test input data and comprehensive testing internal testing, find out product quality problems through real machine verification and other methods, provide development analysis solutions, improve app quality, and supplement the traditional test foundation field. Abnormal online data input to improve app quality.
Mobile app function abnormal reporting refers to recording functional errors, program crash and ANR and other information encountered during the running of the program and reporting these information to the background. Mobile app function abnormal reporting is the basis of online data testing. Only reports can be used to record problems encountered by users in the process of using the app. The comprehensiveness of the reported information determines the validity of the reported data. Generally, the reported information can be counted in the following ways.
Video agency uploads app for free and submits app development features.
1. Unfamiliar dating: From the analysis of download data from multiple markets, many video social APP development companies are keen to enter the field of strange dating, especially in the vertical segment of strange dating. There is room for such applications to exist.
2. Social relationship chain integration: Social APP can integrate multiple relationships and display a simple interface, similar to WeChat APP, which deeply integrates multiple social relationships.
3. Circle of acquaintances: The development of the mobile Internet has established a communication bridge in the field of acquaintance relations. From the current situation in this field, Tencent and Sina are undoubtedly far ahead, while Tencent uses mobile QQ, WeChat, and Friends Network to deepen the relationship. Even better.
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