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Data-driven testing (DDT) is a methodology in which iterative repetition of the same sequence of test steps are performed with the help of a data source in order to drive the input values of those steps and/or the expected values while verification steps are performed. The environment settings and control in the case of data-driven testing are not hard-coded. In other words, data-driven testing is the building of a test script to execute together with all their related data sets in a framework, which makes use of reusable test logic. Data-driven testing provides advantages like reusability, repeatability, separation of test logic from test data and reduction of the number of test cases.
The data sources used in data-driven testing can be Excel files, CSV files, datapools, ADO objects or ODBC sources. In data-driven testing, the following operations are performed in iteration:
There are some advantages associated with data-driven testing. It helps in improving the test coverage as test scripts can be created simultaneously along with application development. Redundancy and any other duplication of automated testing scripts get largely reduced due to inputs and verification processes as well as due to the modular type of design. Considering the cost aspect, data-driven testing is cheaper for automation although it is more expensive in the case of manual testing. In data-driven testing, better error handling is possible and the test scripts are more robust.
However, there are a few drawbacks associated with data-driven testing. Greater expertise of scripting language is required, and a database is required for all the test data at all times.