Software development requires testing as a necessary component in the technologically advanced world of today.
Before the programme is made available to end users, it makes sure that it is of the highest caliber and error-free.
We will go into great detail about data-driven testing in this post, including its concept, advantages, and application.
What Is Automated Data-Driven Testing?
Consider a situation where an application with several input fields has to have a test automated.
Normally, you’d hardcode those parameters and run the test for one-off cases.
Hardcoding, however, is not scalable. When you have to run through a large number of permutations of permissible input values for best-case, worst-case, positive, and negative test situations, hardcoding inputs will rapidly become unmanageable, confusing, and awkward.
A spreadsheet may be used to store and record all test input data, allowing the test to be programmed to “read” the input values from it. That’s precisely the goal of data-driven testing.
By separating test data (input values) from test logic (script), DDT facilitates the creation, editing, usage, and scale-up management of both.
To compare actual and predicted outcomes for validations, automated data driven testing is a methodology that involves a series of automated test steps (structured in test scripts as reusable test logic) that are performed repeatedly for various data permutations (taken from data sources).
The four steps of parameterized testing are as follows:
- Obtaining input data from databases or other data sources such as.xls, .csv, and.xml files.
- Using automated test scripts and variables to enter input data into the application under test, or AUT.
- Contrasting the predicted and actual outputs.
- Applying the subsequent row of data (from the same source) to the same test once more.
Advantages of Automated Data-Driven Testing
- Improved Productivity: Automated data-driven testing brings down the time and exertion required for manual testing by empowering testers to automate monotonous testing processes.
- Expanded Test Inclusion: Data-driven testing permits analyzers to check that the program fulfills all measures by permitting them to test different situations utilizing different informational collections.
- Early detection of bugs: Data-driven testing saves time and costs by empowering testers to find defects from the get-go in the improvement cycle.
- Reusability: At the point when test information is reusable, it requires less investment and work to foster new experiments.
- Easy to Maintain: Refreshing and keeping up with test information is basic and doesn’t require changing test scripts since it is put away freely from the contents.
Applying Automated Data-Driven Testing
Data Sources: CSV documents, XML records, data sets, and different sources might give information used in data-driven testing. These data sources contain the various input sets that will be utilized for testing. They permit you to alter and expand the scope of the test as required.
Test Design: Careful test design is necessary to provide efficient data-driven testing. Every test case should have a particular set of data and an expected result. As an example, in our cross-regional site case, testing may include inputs that match to different address formats, and expected results would take into consideration proper processing in each location.
Automation Frameworks and Tools: Data driven automation testing is supported by an abundance of frameworks for automation and testing tools. These solutions produce thorough reports, make it easier to conduct tests, and interact with many data sources in a smooth manner. Cucumber with scenario outlines, JUnit with parameterized tests, and Selenium with TestNG are a few well-known examples.
Limitation Of Automated Data-Driven Testing
Although DDT’s technique has several drawbacks, it does enable scaling.
- Finding the “correct data set” is challenging when you are checking data continually in a cycle! Data validations are laborious procedures, and the SDETs’ automation abilities determine how well the tests turn out.
- Even though DDT keeps test data and scripts apart, it’s crucial to finish the work before it’s time. A huge dataset may occasionally have the script programmed by SDETs to only test up to a predetermined number of rows in order to save time. This allows for failures with further rows of input data.
- A tester’s lack of familiarity with a programming language might make error debugging challenging, even in a DDT environment. Typically, they wouldn’t be able to spot logical mistakes when a script is executing and raising an exception.
- An increase in the amount of documents. Because DDT takes a modular approach to testing, there is going to be a greater need to document them so that everyone on the team is aware of the process and structure of the automation and framework. Such documentation would include topics like infrastructure for script management, testing outcomes at various testing levels, and so on.
Some hygiene tips for Automated data-driven testing:
- Strive to develop tests that generate scenario test data on their own.
- Make that the database isn’t permanently altered by the testing.
- Tests covering both positive and negative test situations should be included.
- Write automated test scripts that are adaptable to different data settings with ease.
- Organise test data for each test case into a single storage format, such as a table or an excel spreadsheet.
- For any faults in the test execution caused by problems with the data, consult the test result report that was created.
Conclusion
Effective and efficient testing requires automating data-driven testing. You may enhance the quality of your software and automate data-driven testing by following the above-described methods.
You may shorten the testing period and effort needed, improve test coverage, and find bugs early in the development cycle by automating data-driven testing.
Although careful preparation and execution are necessary for automating data-driven testing, the results are well worth the effort.