Abstract
Software engineering always strives to develop and identify software pitfalls and errors before publishing the software product, in testing the software. Bugs can appear during any stage of development or testing, even after the product has been released. This paper describes different methodologies for data flow testing. Since testing is the process of running a program to identify errors, we need to increase the accuracy of the coverage area by including dataflow elements based on aliases and avoiding useless elements that reduce the overall coverage to increase the applicability and effectiveness of the dataflow test. This page looks at data flow testing, which is a type of basic test (white box). Information flow testing is divided into two main points: properties / usage test and a set of tests embedding measurements; And divide the program into parts according to its factors to make testing programming frameworks more straightforward. It also describes the steps for performing data flow testing as well as how to design test suites that take anomalies into account. It also examines and discusses methods used to date to perform data flow testing. These approaches include node-based design, trend-finding coverage, web application comparison, and analytical testing.
