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. Rapise provides strong support for DDT with the ability to load in sets of test data from an Excel spreadsheet or relational database.
Data Driven Testing is an automated testing technique in which test case data is separated from test case logic. Each set of test case data consists of input values and a set of expected output values. The actual output values are compared to the expected output values to determine whether the test passed.
The built-in Spreadsheet object lets you implement data-driven tests with a Microsoft Excel spreadsheet. It allows you to connect to, query, and read an excel spreadsheet from your test script.
This lets you create a generic test script that can be called with specific sets of data to run the same set of tests with different test data. The output from the test can then be compared with the expected result included in the spreadsheet.
The spreadsheet editor in Rapise provides a full set of data manipulation and formatting options, so there is no need to install or use an external editor (e.g. Excel). This simplifies its use on dedicated testing machines that may not have Microsoft Office licenses:
Using the Rapise Visual Language (RVL) allows you to have a set of RVL sheets and data spreadsheets all working together without any code. All you need to do is tell Rapise how your data spreadsheet maps into the columns used by the RVL loop and it does the rest:
Rapise comes with the Database query global object that allows you to send SQL queries to a relational database (ODBC, OLEDB, etc.) and then iterate through the results as part of your test script:
This lets you create a generic test script that can be called with specific sets of data to run the same set of tests with different test data. The output from the test can then be compared with the expected result included in the database.
In addition, you can query the external relational database to compare the results of the test being performed with the expected results stored in the database.