In the software development lifecycle, software testings is a major step and play a key role in addressing bugs. This is the stage that defines the success of software being developed and if it serves the purpose for which it is being executed.
While there are several software testing tools available in the market, not all can afford those. Therefore at times, software engineers rely on manual efforts and tend to make errors. To avoid such errors, data-driven testing is another automated software testing procedure that iÂÂs gaining prominence.
That said, nowadays we are offered a plethora of automation testing tools and frameworks to carry out data-driven tests. However, the question here arises as to how these tests are beneficial for us. To answer this key question, here is a guide that tells us about the advantages that data-driven automation offers.
Data-driven automation testing
Data-driven automation testing is a framework wherein the test case code or script is presented. The test script can be presented through a plethora of mediums such as table format in a .xls, .xml, or CSV database. Further, after compiling the data set, the data table from which a test run has to be taken is separated. The separation of data is undertaken considering testers frequently deal with multiple testing data sets and a particular test script is used to derive a particular outcome. The location of the required data table can be set apart from the data set as that these together are not contingent on each other.
While test data and test scripts both are self-supporting, they can be placed, altered, added, or removed, causing no impact on each other.
How to undertake a data-driven testing procedure?
As we understand what Data-Driven testing is, engineers need to be aware of the right procedure to derive the right outcome. The procedure begins with choosing the data table to end in a phased manner.
Choosing the data table: Depending on the complexity of the test, the source for the data table has to be chosen. For instance, for conducting a simple test related to passwords and usernames CSV files would work well but for complex testings with data ranging different variables, XML file type would be suitable for large automated test suites
Linking the data: Connecting the data source to the test is the next and important step. For populating the test data present in the AUT (application under test), one can import the test files. For instance for Selenium tests in Python, linking data for a test could be relatively easy as CSV files are used but for complex scenarios with XML as a data source, the script can be imported post which data has to be extracted to get the expected result
Execution: Test actions are executed in accordance with the pre-defined test script. The tests will then operate in a loop
Assessing the result: The test results from data-driven testing can quickly show the developers the test case with a pass or fail. But still, many developers find assessment of test results a challenge. To overcome this, one way would be to check all the possible outcomes using a case statement
The output that is expected is also recorded in the datasheet. This is because the automation script goes through the probable outcome, collates and differentiates with the original production, and fleck the test case with the pass or fail report.
Tools for data-driven automation testing
Selenium, TestNG, Apache POI (for reading or writing in Excel), QTP, Test Sigma, Katalon Studio, and TestComplete are some of the commonly used tools for data-driven testing.
How beneficial is Data-Driven automation testing?
Reusability: Once the test scripts are ready, it can be used for several test inputs. As tests are conducted in loops, this eliminates the need to make alterations in the script, as it will only read the test data present in the file. In case the test data is hardcoded, the array of code elevates tremendously. This helps in the reusability of the program.
Intensified clarity: As there are independent folders of test data and scripts, you can find a clear picture of the development and maintenance stages. In case the test data needs any alterations, you can do it quickly. You do not need to refer to the script or vice versa.
Serviceability:If the test script or data needs any update, you can do it without complicating the data. This is because both of them are kept aside from each other. While a test engineer works on the test script, any team member knowing AUT can easily maintain and support the test data file. Again, it does not need to depend on test scripts.
Wider test-coverage: Offers excellent test coverage and clearly reflects the actual generation of data with great quality test data. This further allows for achieving better and high stability and quality of the app. As a result, minor defects can be in the environment of production. This is because each of the possible data will already have been tested, and all the issues would have been fixed.
Systematic: When developers are not working, a systematic test automation run can be set up. Later, one can go through the results at a different point in time saving time. In addition to that, when data-driven automation is compared with manual testing, the former is comparatively faster and swifter.
Decreased amount of manual error: The test data is not entered manually in the app. Instead, the test data is fed from a file that is either XML or Xls format. Besides that, when a massive amount of data is populated, human error is inevitable. Data-driven automation eliminates the chance of error. As a result, the desired product is of immensely high quality and efficiency
Rapid decision making: Enables rapid decision making as the quick process of automated execution of a wider set of data allows the developer to comprehend decisions related to the defects and errors rapidly.
Conclusion
The entire development process of a data-driven testing framework is self-reliant. A developer with sound technical knowledge can use any automation tool like testsigma for DDT as the process is a cakewalk one data script and data table is in place. That said, the type and quality of data being used hold the utmost significance. The data set being used therefore has to be realistic to avoid false right results. With data-driven automation, any gap that possibly exists in the code will be spotted thus ensuring efficient testing and software development. As a result, development teams that rely heavily on automation make use of data-driven solutions to minimizes all the probable causes of defects and any risk in the production process thus making their lives easier
