Data-driven testing: Difference between revisions

Revision as of 16:23, 22 May 2016

Revision as of 19:52, 3 August 2016

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==Introduction==

In the testing of [[Computer software|software]] or [[Computer program|programs]], several methodologies are available for implementing this testing. Each of these methods co-exist because they differ in the effort required to create and subsequently maintain. The advantage of Data-driven testing is the ease to add additional inputs to the table when new partitions are discovered or added to the product or [[Systemsystem Underunder Testtest]]. The cost aspect makes DDT cheap for automation but expensive for manual testing.

==Methodology Overviewoverview==

Data-driven testing is the creation of test scripts to run together with their related data sets in a framework. The framework provides re-usable test logic to reduce maintenance and improve test coverage. Input and result (test criteria) data values can be stored in one or more central data sources or [[database]]s, the actual format and organisation can be implementation specific.

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Navigation through the [[System Under Test|program]], reading of the data sources, and logging of test status and information are all coded in the test script.

==Data Drivendriven==

Anything that has a potential to change (also called "variability," and includes elements such as environment, end points, test data, locations, etc.) is separated out from the test logic (scripts) and moved into an 'external asset'. This can be a configuration or test dataset. The logic executed in the script is dictated by the data values. [[Keyword-driven testing]] is similar except that the test case is contained in the set of data values and not embedded or "hard-coded" in the test script itself. The script is simply a "driver" (or delivery mechanism) for the data that is held in the data source.