Biblio
Big Data Platform provides business units with data platforms, data products and data services by integrating all data to fully analyze and exploit the intrinsic value of data. Data accessed by big data platforms may include many users' privacy and sensitive information, such as the user's hotel stay history, user payment information, etc., which is at risk of leakage. This paper first analyzes the risks of data leakage, then introduces in detail the theoretical basis and common methods of data desensitization technology, and finally puts forward a set of effective market subject credit supervision application based on asccii, which is committed to solving the problems of insufficient breadth and depth of data utilization for enterprises involved, the problems of lagging regulatory laws and standards, the problems of separating credit construction and market supervision business, and the credit constraints of data governance.
Mobile app developers today have a hard decision to make: to independently develop native apps for different operating systems or to develop an app that is cross-platform compatible. The availability of different tools and approaches to support cross-platform app development only makes the decision harder. In this study, we used user reviews of apps to empirically understand the relationship (if any) between the approach used in the development of an app and its perceived quality. We used Natural Language Processing (NLP) models to classify 787,228 user reviews of the Android version and iOS version of 50 apps as complaints in one of four quality concerns: performance, usability, security, and reliability. We found that hybrid apps (on both Android and iOS platforms) tend to be more prone to user complaints than interpreted/generated apps. In a study of Facebook, an app that underwent a change in development approach from hybrid to native, we found that change in the development approach was accompanied by a reduction in user complaints about performance and reliability.
Code coverage is a widely used measure to determine how thoroughly an application is tested. There are many tools available for different languages. However, to the best of our knowledge, most of them focus on unit testing and ignore end-to-end tests with ui- or web tests. Furthermore, there is no support for determining code coverage of transcompiled cross-platform applications. This kind of application is written in one language, but compiled to and executed in a different programming language. Besides, it may run on a different platform. In this paper, we propose a new code coverage testing method that calculates the code coverage of any kind of test (unit-, integration- or ui-/web-test) for any type of (transcompiled) applications (desktop, web or mobile application). Developers obtain information about which parts of the source code are uncovered by tests. The basis of our approach is generic and may be applied in numerous programming languages based on an abstract syntax tree. We present our approach for any-kind-applications developed in Java and evaluate our tool on a web application created with Google Web Toolkit, on standard desktop applications, and on some small Java applications that use the Swing library to create user interfaces. Our results show that our tool is able to judge the code coverage of any kind of test. In particular, our tool is independent of the unit- or ui-/web test-framework in use. The runtime performance is promising although it is not as fast as already existing tools in the area of unit-testing.