Data Driven Testing
BeatBlip enables organizations to build highly repeatable and easily maintainable scripts by allowing the complete segregation of Test Input and Expected Output Data from the Test Scripts. It allows organizations to easily and aggressively test their products, by executing the same test scenarios, under a variety of data conditions and environment settings.
BeatBlip’s approach to data driven testing is rooted in its flexibility, easy maintenance, and enhanced repeatability. BeatBlip’s Data Manager not only allows testers to externalize test data and associated expected outputs from the test scripts but it also gives them the flexibility to leverage any data source. It supports the following as test data input sources:
- Data Files: BeatBlip allows users to specify their test data in multiple file formats (e.g. Excel, CSV, and PDF). The files can then be brought into the system as data-files or their data can be imported into BeatBlip as records of datasets. In other words, users have the flexibility to read from the files as the test executes or use the pre-loaded data from these files to drive the test execution.
- Database Systems: BeatBlip also enables users to read data directly from the database systems. It supports both SQL and NoSQL databases. The database queries are kept external to the test scripts just like the imported data, thus any change to the DB schema or the data query does not impact the test scripts.
- Application UIs: In some test scenarios the test data needs to be dynamic. It’s only available at run time when the script executes and needs to be picked up from a Web Page or a Mobile Screen. BeatBlip allows users to capture and use data from the application UI itself. This application can be the application under test (AUT) or an entirely different application.
- API Calls: Often testing requires pulling input data from internal or third-party systems via API calls. BeatBlip supports seamless integration with API based systems so that testers can easily invoke API calls and use the retrieved results as test input to their scripts.
Data Driven Testing (DDT) automation emphasizes on the test data. It enables the creation of test scripts wherein test data and/or output values are directly read from data files, database, application UI, or an API call instead of using the same hard-coded values each time the test is run. Each test is established using a table of conditions as test inputs and verifiable outputs, without hard coding the environment and control settings.
Consequently, the table of conditions directly avoids hard coding, allowing a QA tester to build generic test scripts that can be executed with any provided test input data. If the scenario needs to be tested under newer conditions, all that the tester needs to do is add that data-condition along with the expected output to the input data associated with the test script. The test steps of the scripts do not change, only the associated test data expands or shrinks based upon the need.
As a part of their daily tasks, the product development teams make several changes in their products necessitating enhanced testing. The changes introduced typically require testing of even the existing functionality under newer or modified data conditions. As a result, it becomes difficult to run a test with hard-coded data each time. In manual testing, a tester will have to design multiple test scripts or modify existing test scripts multiple times and run them individually. This becomes a tedious and monotonous task for a manual tester.
That’s exactly where data driven testing helps organizations and their QA teams. It helps testers manage a large volume of test data. By using this approach, you can overcome the issue of running different test scripts for multiple data sets. It keeps the external data and functional tests separately and loads them when needed to extend the automation testing.
- Separation of Test Cases and Test Data: Enables testers to test their applications using different sets of data values and parameters without the need for changing the test script/cases. Making a change in data sets (like addition, or deletion), doesn’t have any implication on the test cases.
- Reduced Execution Time: Helps in the rapid execution of a large volume of test cases, especially repetitive ones that cover positive and negative test cases, or corner, edge, and boundary cases.
- Reusability: Allows automation test engineers to run a test script thousands of times with different data sets each time.
- Realistic Insights: Offers QA teams with realistic results and finds defects that might get missed otherwise.
- Efficiency: Reduces manual efforts and makes it easy to maintain, monitor, and track results.
- Stronger Test Coverage: Improves regression testing and offers better coverage as large volumes of data can be managed and tested against.
- Less Redundancy: Reduces unnecessary duplication of tests. Various data sets can be used as inputs to run a single test script but can also be used in several test cases.
- Improves System Usage: Test cases can be executed at night when test servers would otherwise be idle.