A Guide to Data-Driven Testing

Data-driven testing is a popular testing method that works off of data specifically stored in a table or spreadsheet format. It is a type of testing that allows developers to input a test script that will execute tests for all the data from a table and place the results in that same table.

It is often used when testers have multiple data tests that they want to test within a single element, especially since it can be time-consuming to create individual tests for each separate data set. With data-driven testing, you can keep the data separate from the test scripts so that the same script can be used for different combinations of input data.

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What Is Data-Driven Testing?

Data-driven tests consist of two parts: the procedure and the data. The procedure is the set of steps that must be performed, and the data is the actual data that is used within the test.

A common type of data-driven testing is a login test. In this testing style, you create a set of usernames and passwords that would serve as test cases for your login page. You would then store this information in a table where the program could run a test trying to log in utilizing each row in the table. Then, you could check the test’s results against the expected outcomes, whether they be valid or invalid.

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The Benefits of Data-Driven Testing

There are a number of benefits that will help your team become more efficient and productive.

Instant productivity

Increased Test Coverage

Data-driven testing allows for testing a wide range of scenarios and inputs to increase overall test coverage. By testing with various data sets, you can uncover more bugs and ensure better software quality. This approach ensures that your software is thoroughly tested under different conditions, including edge cases and boundary conditions, which might be overlooked in manual testing.

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Better Reusability

With data-driven testing, you can separate test scripts from test data. This separation enables the same test script to be reused with different sets of data, saving time and effort in test creation and maintenance. Test scripts become more modular and easier to maintain as changes to the test logic can be made in one place and applied to multiple data sets.

Instant productivity

Improved Efficiency

Data-driven testing automates the process of running tests with different data sets. This automation leads to faster test execution and more efficient testing processes. Testers can run a large number of test cases quickly, allowing for more thorough testing in a shorter amount of time. Additionally, automated test execution reduces the risk of human errors, resulting in more reliable test results.

The Challenges of Data-Driven Testing

There are several inherent challenges to data-driven testing to keep in mind.

What is a data-driven testing framework?

Data Management

Managing large volumes of test data can be challenging. It’s important to organize and maintain test data effectively to ensure accurate and reliable test results. This includes ensuring that test data is up-to-date, relevant to the test scenarios, and properly formatted for use in test scripts. Without proper data management practices, data-driven testing can lead to inaccurate test results and inefficient testing processes.

Maintenance Overhead

As the application evolves, test data and scripts may need to be updated frequently. This can lead to increased maintenance overhead, especially when dealing with complex test scenarios. Changes in the application’s functionality or user interface may require updates to test data and scripts, which can be time-consuming and lead to errors. To minimize maintenance overhead, your team needs to design test scripts and data structures that are flexible and easy to update.

Resource Intensive

Data-driven testing can be resource-intensive, especially when dealing with large data sets or complex test scenarios. Running data-driven tests can require significant computing resources, such as memory and processing power, which can impact the overall efficiency of the testing process. Additionally, managing and maintaining test data repositories can require dedicated resources and infrastructure.

What is a data-driven testing framework?

How to Implement Data-Driven Testing

Here’s our step-by-step guide to implementing data-driven testing.

Identify Test Scenarios

Begin by identifying which test scenarios can benefit from data-driven testing. These are typically scenarios where the same set of actions or operations need to be performed with different sets of data, such as testing a login functionality with multiple usernames and passwords.

Design Test Data

Once you’ve identified the test scenarios, design the test data that will be used in these scenarios. This includes defining the range of data values and any special cases that need to be tested. For our login functionality example, you would create a set of valid and invalid usernames and passwords.

Separate Test Data from Test Logic

It’s important to store the test data separately from the test logic (the actual test scripts). This separation allows you to easily update or change the test data without modifying the test scripts. Common ways to store test data include CSV files, Excel spreadsheets, or databases.

Create Test Scripts

Develop the test scripts that will execute the test scenarios using the data. These scripts should be designed to read the test data from the external source (e.g., CSV file) and use it to perform the necessary actions in the application under test. Just make sure that your test scripts are flexible enough to handle different data sets and scenarios.

Execute Tests

Now it’s time to run the data-driven tests using your test scripts and data. During the test execution, the test scripts will iterate through the test data, performing the specified actions and checking the expected outcomes. Monitor the test execution to ensure that it proceeds as expected.

Analyze Results

After the test execution is complete, you can analyze the test results to identify any issues or failures. If a test fails, investigate the cause of the failure and make any necessary adjustments to the test data or test scripts. It’s important that you document any issues found during the testing process.

Iterate and Improve

You will improve productivity and efficiency by continuously refining your data-driven testing approach based on the insights gained from each test cycle. This can include updating your test data for new scenarios or edge cases, improving your test scripts for better coverage, or enhancing your test environment to simulate real-world conditions more accurately.

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