This guide provides an overview of data-driven testing, its advantages, disadvantages, and some best practice tips.
We've talked about the importance of synthetic data (especially AI-generated synthetic test data like what's generated right in Virtuoso) and how it can help your testing, but what specifically can you do with this data? Well, data-driven testing (DDT) is the perfect way to use this synthetic data to ensure that your web application can handle anything thrown at it. Read on to learn about DDT and its benefits!
DDT uses data to run functional tests faster and more efficiently. Rather than writing an individual test case for each scenario, DDT separates test logic from test data, allowing for the creation of reusable tests that can be executed with different data sets. Typically, data is stored in an external spreadsheet or CSV file, but if you're using the right testing software, you can often store and even generate synthetic test data right in the application. Essentially, DDT runs the same test with different data sets to ensure that varying or edge-case data doesn't break your app.
End users aren't perfect, and you can count on some accidentally entering the wrong data into different fields. One might accidentally put numbers from a birth date into a name field, or a European user might enter the month and day numbers backward into an American application. A forgotten line of code could make your entire application crash instead of a tidy red asterisk and error message telling them to check their input values.
DDT is a good option for your testing and can benefit your tests in many ways.
1. You can easily test your application with multiple sets of data to test every possibility.
2. Gone are the days of adding test data to every test you run manually. The data tables used in DDT are completely reusable, so when you're creating your tests, you don’t have to create data sets from scratch for each one.
3. DDT increases the scalability of your tests through its reusability factor. When more tests are created to run on more forms (or whatever you're running your DDTs on), you can keep using your data tables over and over in new tests.
4. Entering test data manually into each test isn't exactly interesting. By allocating the somewhat boring task of adding and cycling through test data to automation, testers are freed up to focus on creating more efficient tests, conducting exploratory testing, or anything else they'd rather be doing.
5. DDT can be performed during any testing phase, even in the earliest stages of development to ensure functionality is working before moving on to another area.
While DDT is a great way to make sure that your application is processing data correctly, it does have its drawbacks.
1. If you're using a coded automation framework, your testers might need a higher level of knowledge of scripting languages to be able to create DDTs.
2. DDT requires more documentation to ensure that tests are managed correctly and testing results are properly recorded and reported.
Setting up DDTs that work correctly can be complicated, so here are some best practices to keep in mind:
1. You should strive to use data that reflects real-world data as accurately as possible, including common errors users might make.
2. Be sure to test both positive and negative outcomes - positive data sets will, for instance, log the user in successfully, while negative data sets will cause the login to fail.
3. Reuse data tables to test security and performance as well.
