What is Data Driven Testing?

What is data-driven testing?

It is a software testing method where the table of conditions is directly used as test inputs and verifiable outputs, and also the control and test environment settings are not hardcoded. This means that the result data values, input, test criteria are stored in data sources such as ADO objects, excel files, ODBC sources, CSV files etc.

Through the data-driven testing platform, a number of data sets can run on the same tests. If a different test needs to be created for each data set value, especially when it comes to hard-coded values, it becomes really difficult to maintain and modify. But, when it comes to the implementation of data-driven testing, the data is separated from functional tests, so that the same test script can be executed for different combinations.

Since all the information is properly documented, the test scripts can be generated with less code. Through this test, coverage is improved and unnecessary duplication of test scripts is reduced.

Data-driven testing is a methodology where a sequence of test steps in order to run repeatedly is automated. Data with different permutations are used so that actual and expected results can be compared for validations.  

The importance of data-driven testing:

Multiple data sets are frequently used by testers for a single test and for each data set creating individual tests can prove to be time-consuming. Through the implementation of data-driven testing, data is kept separate from test scripts, so that execution of the same test scripts can happen for different combinations of input test data and test results can be efficiently generated.

What is a data-driven framework?

It is an automation testing framework where data files are used from which input values are read and stored into variables in test scripts. Testers are enabled to build both positive and negative test cases into a single test. In a data-driven framework, input data can be stored in a single or multiple data source like .csv, .xml, .xls, and databases.

Data-driven testing best practices:

• Realistic information can be used during the data-driven testing process

• To drive dynamic assertions, data can be used

• Both positive and negative outcomes need to be tested 

• Data-driven functional tests to need to be repurposed for performance and security 

• Virtual APIs need to be driven with meaningful data.

Advantages of Data-driven testing:

• Application with multiple sets of data values can be tested during regression testing 

• In just one file, both verification data and test data can be organized

• In different tests, functions and actions can be reused

• Test data is automatically generated using some tools. This is especially useful when random test data of large volumes are necessary, which in turn saves time

• The logic of test cases/scripts can be separated by testers and developers from the test data 

• Execution of the same test cases can happen several times, which in turn helps reduce test cases and scripts

• The test data is not affected by changes in the test script.

Conclusion: If you are looking forward to getting professional and strategic advice on data-driven testing in order to drive your specific project, then just visit online a leading software testing company that will provide you a viable testing methodology that is precisely in line with your project requirements.