Data-driven testing (DDT) is a term used in the testing of computer software to describe testing done using a table of conditions directly as test inputs and verifiable outputs as well as the process where test environment settings and control are not hard-coded. In the simplest form the tester supplies the inputs from a row in the table and expects the outputs which occur in the same row. The table typically contains values which correspond to boundary or partition input spaces. In the control methodology, test configuration is "read" from a database.
Introduction
In the testing of software or 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 SUT. The cost aspect makes DDT cheap for automation but expensive for manual testing. One could confuse DDT with Table-driven testing, which this article needs to separate more clearly in future.
Methodology Overview
Definition: 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 databases, the actual format and organization can be implementation specific.
The data comprises variables used for both input values and output verification values. In advanced (mature) automation environments data can be harvested from a running system using a purpose-built custom tool or sniffer, the DDT framework thus performs playback of harvested data producing a powerful automated regression testing tool. Navigation through the program, reading of the data sources, and logging of test status and information are all coded in the test script.
Data Driven
Anything that has a potential to change (also called "Variability" and includes such as environment, end points, test data and 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.
The databases used for data-driven testing can include:-
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