What Is Data-Driven Automation?
Data-driven testing facilitates test procedures, demonstrates more capability in discovering bugs, and ensures quality with logical input to assure successful deployment.
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There are a number of routes that developers and quality assurance teams can take to leverage automation in their operations. Data-driven automation is one of the many processes that can be adopted for these needs. However, what exactly is this approach and what considerations does it entail? In this piece, we'll explore what data-driven test automation is and how to effectively use it in an increasingly Agile development environment.
Defining Data-Driven Testing
Data-driven testing automation places test data as the prime factor. Automation Repository noted that this method enables test scripts to interface with different sets of data without needing changes to the test case. Each test is established by 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 simply input data from a row in the table with anticipated output occurring in the same row. The defining variable is location rather than specified data strings. As an alternative to testing that is code-defined, data-driven testing reads values as they are related to boundary or partition input locations.
Test scripts in data-driven testing run simultaneously with related data sets in a test framework of reusable test rationale that reduces the need for maintenance and improves test coverage. Test implementation is reliant on the test format rather than data-specific pre-requisites. The data-driven table also (DDT) subsequently harvests data playbacks for empowered regression testing.
What Exactly Does This Mean?
Basically, data-driven by specifying a set of data, the test script will enable test case execution within a data-driven table. The values are not likely to cause any errors, but if a data variation was never attempted or new information appears, it could potentially raise flags. QA teams must understand this type of roadblock if they are to address it effectively and successfully leverage data-driven automation.
Adopting Data-Driven Automation
While there is some strategizing involved, aligned with test management tools, data-driven automation can be an essential asset. Forbes contributor Brian Ascher noted that data-driven processes can fundamentally change how development is approached by providing more insight for informed decision-making, thereby benefitting the entire enterprise.
"Data-driven solutions make predictions about business functions, prescribe what to do next and in many cases take action autonomously," Ascher wrote. "Trained analysts are not required to query databases. Instead, business users get answers directly from the software. These answers typically feed seamlessly into the flow of business activity, often invisibly."
This type of information can yield a clearer picture of where projects and test cases can progress from their current states. By using data-driven automation, QA teams can have a steady flow of objective numbers that will give them an idea of what areas need to be changed and what is being done successfully from any table source, including an Excel spreadsheet, with benefits that allow QA testers to:
Creating Data-Driven Tests
- Pull data in from an external source data set.
- Repeat steps in test cases (you don’t have to record individual test scripts for each test case).
- Take data from one record.
- Make data loops from a data record.
- Create data for variable orders.
- Navigate to a file.
- Iterate a subset of records.
Instead of a normal framework where code and data are part of a single unit, data-driven tests list the two elements as separate items. The data is hosted in external files and is fetched by the code when automated testing is executed. Data columns, rather than the data itself, are associated with test actions. To run a data-driven test, take the following steps.
Associate each column with a specific test step. Click Finish.
The data-driven format runs the column location tests.
The last test step is the checkpoint. It verifies the total value of each record subset, creating a data-driven loop.
The data-driven loop reads the data to repeat test actions for every row of data in the source document, such as an Excel spreadsheet.
Total values populate the spreadsheet (or data source) from data-driven formats, rather than from coded data.
Because the information isn't hard coded into the test case, the script can easily be reused any number of times with different data assets.
Industry expert Ville-Veikko Helppi noted that because of the separation of test case and data, the number of test scripts needed is reduced and testers can easily change elements without affecting the simplicity of the tests.
"Testers can test their app using lots of different data values and parameters without building any complexity in test case/script themselves," Helppi wrote. "Also, the data asset is easy to add, remove and configure and doesn't have any implication to test cases."
Benefits of Data-Driven Automation
With data-driven testing, the test script simply drives or delivers the table of formats into data-driven implementation. QA testers can initiate testing while development is still in process. Additionally, redundancies are greatly reduced, resulting from the modular action of the table test design. The use of the same files and records for both input and output verification goes far towards reducing duplications.
Automation allows that test results to be conveniently stored as text records for ease of reuse. In addition, Boolean TRUE/FALSE values (rather than reactive aborts) allow for a better handling of errors, resulting in test scripts that are more robust and more smoothly executable.
Data-driven testing deservedly boasts of separating test case and test script logic from the data, allowing QA testers to evaluate contrasting data parameters and values while preventing an increase in test case or test script complexities in that the data syntax has no implication in the testing process. The data resource can be easily added, configured, or removed.
The ability to change test data at will for instant use in testing provides QA teams with a realistic test environment that has the potential to discover defects which could otherwise remain hidden. Modular testing that relies on data assets rather than data syntax to easily add, configure, or remove test faculties, can actually perform, when appropriate, with less test cases or scripts. Needless duplications are also reduced, resulting in optimized modularity in testing.
Ville-Veikko Helppi of Bitbar maintains that “Data-driven is like automating your test automation.” Data can be stored in a file, on a spreadsheet, in a database, or on your desktop, then utilized as needed to test using format specifics for the assessment of logical outcomes.
To support enterprise strategic goals and ROI, QA testing environments and procedures have as their primary goal the timely release of quality software products. Data-driven testing facilitates test procedures, demonstrates increased capability in discovering bugs, and can more efficiently ensure product quality with logical input that further assures successful deployment.
Not only will a data-driven approach to Agile testing give insight to critical developments, but decision-makers can actively use this information to solve business problems. A data-driven app for a retailer, for example, should actively send alerts when an item is predicted to be out of stock so that the organization can order more. Data-driven automation should have a similar function by consistently notifying teams about errors, changes and other trends while providing insight on the best path to pursue. As Ascher noted, simply having the data doesn't matter if it's not used.
Published at DZone with permission of Sanjay Zalavadia, DZone MVB. See the original article here.
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