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Data-Driven Testing: How to Bring Your QA Process to the Next Level?

Have you ever encountered a test case where you need to test the same scenario multiple times by changing the input data? In such a situation, manually changing the test data each time can be tedious, especially if the test itself is automated. It would help if you had a way to build your test data so that you can run a given test with varying inputs, and this is precisely what the data-driven testing approach is all about. If you often find yourself needing to work with different permutations of parameter values, then this is your way ahead.

What is data-driven testing?

Data-driven testing is a great way to optimize your testing strategy. This approach focuses on segregating the test logic from the data set. This helps decouple the two so that changes can be made easily to necessary portions of the tests. You could provide this data set in the form of files that are in XML, SQL database, Excel sheets, CSV, or other formats. Through this form of testing, the expectation is to mimic real-world data formats so that the system is thoroughly vetted before release. This can be achieved by mimicking production databases using data masking or using services or methods that generate such data.

For example, you need to test a sign-up form that requires the user’s name, phone number, and email address. While doing so, you want to make sure that you also check numerous negative scenarios, such as an invalid phone number or email address format. This means that you want to fill out the same sign-up form multiple times but with different input parameters.

The goal behind data-driven testing is to focus on organizing the code in such a way that the data set values can be sent in as parameters. Hence it is also known as parameterized testing. During run time, for each iteration of the test case, the parameter variables are replaced by data values.

When to use data-driven testing?

This approach is best used when:
  • The same scenario needs to be tested over and over again with different input values.
  • An existing test scenario needs to be improved from a coverage perspective.
  • Negative test cases need to be checked. Thus invalid data rows are added to the data set.

Advantages of data-driven testing

The advantages of data-driven testing are as follows:
  • Due to the segregation of test logic and test data, it is easier to make updates to either. This comes in handy in complex test scenarios where multiple tests are interdependent.
  • It can be used to populate the database for load, performance, or functional testing, or even for demo purposes.
  • Writing duplicate or redundant tests can be avoided using this method.
  • In situations where new issues or use cases are reported, if the test logic already exists, then modifications to the data set can help prevent such issues in the future.
  • If automation is used, then the tests can run for hours without manual intervention, even during off hours.
  • With automation, greater accuracy can be achieved as opposed to a manual tester doing the same activity. It is a repetitive task, and due to the chance of human error, there is a possibility that issues may get leaked, no matter how experienced the tester is.
  • Not having to hard code data makes the test logic concise and also allows for clean and lean data storage methods. This also reduces test maintenance.

Data-driven testing framework

Now that you know when to opt for data-driven testing, let’s look into what needs to be done to implement it.

Step 1: Identify the scenarios that need input in the form of data sets
This can be done by referring to the above sections.

Step 2: Identify a data storage source to save the data set in and connect it
The data set needs to be stored in a place that can be accessed through your code. This compatibility needs to be checked. Also, when doing so, check for specifications of the source like a number of rows that can be supported, type of characters, and so on. These specifications should not hamper your data set.

Step 3: Create test data
The data set can be generated manually or through some tool that creates such data. This data can then be stored in the identified data source.

Step 4: Segregate the code and data set part of the test cases
If there are any hard-coded test values or custom logic to produce such data, now is the time to clean it up. Ideally, your test logic should be concise and take data as input parameters. Also, try to break the logic into reusable chunks so that those methods can be reused.

Step 5: Execute the tests using the proper tools
For this step, ensure that you use an automation tool that serves your needs.

Step 6: Use the results to make necessary changes
Depending on the test logic written and the automation tool used, the before and after states will be saved. These can be used to compare and verify if the expectation is met.

Some pointers for testers

Below are some tips that you can keep in mind when opting for data-driven testing.

  • The most important point to keep in mind is to write test cases and create test data that is insightful and checks for all kinds of vulnerabilities.
  • When writing test cases, aim to write test logic that can have its own test data.
  • Make sure that your tests cover positive and negative scenarios.
  • Be mindful of not changing the dataset with your test logic.
  • Try to be consistent with your chosen data storage. This will help centralize the test data repository.
  • Post-test case execution, refer to the generated test reports to look for errors.

How does testRigor handle data-driven testing?

Among the various tools available in the market to generate and test with data sets involved, testRigor is one such tool. It comes loaded with features and outshines most other tools due to its ability to allow commands in plain English, as well as practically eliminating test maintenance.

When it comes to data-driven testing, testRigor allows you to define both global variables and data sets in the test data section. You can also upload any existing data set files from your computer and use them for your tests.

You can also generate unique test data based on the type of input needed, for example:
generate unique email, then enter into "Email" and save as "newEmail"
Or you can generate unique data based on the format you need by using RegEx:
generate from regex "[A-Z][a-z]{15}", and save as "generatedName"

Feel free to refer to the documentation section and search for more information on testRigor’s features.

Conclusion

Data-driven testing is a great way to expand the test coverage and bring your testing process to the next level. When combined with automation, it can be an extremely efficient way to test thoroughly while also being efficient with reusing the same test case with various inputs. Just be sure that the test scenarios are designed in a way that holds true to the essence of data-driven testing, that is, decoupling test logic and test data.

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