What is Data-Driven Testing - Improve Your Software

Data-driven testing is an approach for software testing in which test data is stored in a table or spreadsheet format. It also allows testers to input a single test script that could execute tests for all test data from a table and expect the test output in the same table. 

What is Data-Driven Framework

The data-driven framework is an automation testing framework in which input values are read from data files and stored in two variables in test scripts. Testers are enabled for building both positive and negative test cases into a single test. We can store input data in the data-driven framework in single or multiple data sources.

Why is Data-Driven Testing Important?

Data-driven testing is important because testers typically have several collections of data for a single test, and it can be very time consuming to construct individual tests for each set. Data-driven testing helps us in keeping data separate from test scripts. 

The same test scripts could then be executed for different combinations of input test data, and test results could be generated efficiently.

Example:

Here is an example of doing so if we want to test the login system with multiple input fields with a thousand different data sets. We can take the following different approaches for testing this.

• Approach 1

Create 1000 scripts, one for each data set, and run each test separately one by one.

• Approach 2

Manually adjust the value in the test script and run it several times.

• Approach 3

Importing data from the Excel sheet. Fetching test data from Excel rows one by one and executing the script. In the given scenarios, the first two hours are laborious and time-consuming. Hence it is ideal for us to follow the third approach. 

How to Create A Data-Driven Automation Framework?

Let’s consider that we want to test the login functionality of an application:

Step 1

Identifying test cases.

• Input Correct username and password – Login Success
• Input incorrect username and correct password – Login Failure
• Input correct username and incorrect password – Login Failure

Step 2

Creating detailed testing Steps for the 3 test cases above.

Step 3

Creating a test script.

If we observe, the test steps remain common throughout the test steps. It will help if we created a test script for executing this step.

// This is Pseudo Code 

// Test Step 1: Launch Application 

driver.get(“URL of the Application”);

// Test Step 2: Enter Username 

txtbox_username.sendKeys(“valid”); 

// Test Step 3: Enter Password 

txtbox_password.sendKeys(“invalid”); 

// Test Step 4: Check Results 

If (Next Screen) print success else Fail

Step 4

Creating an Excel/CSV with the input test data.

Step 5

Modifying the script for looping over input test data. The input commands are also required to be parameterized.

// This is Pseudo Code

// Loop 3 Times

for (i = 0; i & lt; = 3; i++) {

// Read data from Excel and store into variables

int input_1 = ReadExcel(i, 0);

int input_2 = ReadExcel(i, 1);

// Test Step 1: Launch Application

driver.get(“URL of the Application”);

// Test Step 2: Enter Username

txtbox_username.sendKeys(input_1);

// Test Step 3: Enter Password

txtbox_password.sendKeys(input_2);

// Test Step 4: Check Results

If(Next Screen) print success

else Fail

}

The three cases above are the ones that the test script could use to loop over the following test cases by appending test data values to excel.

Best Practices of Data-Driven Testing

Below are the best data-driven test practices:

1. During the data-driven testing process, relevant details may be used
2. In the test script, test flow navigation should be coded
3. Drive meaningful data to virtual APIs
4. Use Data to Drive Dynamic Assertions
5. Good and bad evaluation results
6. Repurpose Data-Driven Functional Tests for Cost and Reliability

Advantages of Data-Driven Testing

Here are some advantages of Data-Driven Testing:

1. Enables several sets of data values to assess the program during the regression testing
2. Test and proof data could only be arranged in a single file, isolated from the logic of the test event.
3. The test script could be contained in a single repository based on the tool. It promotes the comprehension, maintenance, and management of texts.
4. In multiple experiments, actions and functions can be repeated.
5. Certain tools automatically produce test results. It is helpful where massive numbers of time-saving random test data are required.
6. Certain tools automatically produce test results. It is helpful where massive numbers of time-saving random test data are required.
7. Each process of production may be carried out by data-driven research. A single method typically consists of data-driven automation evaluation care. In some test instances, though, we can use it.
8. Helps developers and users to distinguish their test cases/scripts from the test data logic.
9. We should do the same test cases several times to minimize scripts and test cases.
10. Some revisions to the test script would not impact the test data.

Disadvantages of Data-Driven Testing

Here are some disadvantages of Data-Driven Testing:

1. The consistency of the test depends on the implementation team’s automation skills.
2. When checking a huge volume of data, data validation is a time-consuming process.
3. Maintenance is a major challenge, as much data-driven testing coding is required.
4. Technical skills of high quality are appropriate. A tester may need to learn a whole new language for scripting.
5. More documentation will be required. The infrastructure and research results were applied specifically to script management tests.
6. To build and preserve data files, a text editor such as Notepad is appropriate.

Conclusion

Output data can be stored in one or more data repositories, including XLS, XML, CSV, and databases, as part of a data-driven test automation framework. A long and time-consuming process is used to construct a single test for each data set. 

The Data-Based Research Framework addresses this problem by distinguishing data from practical testing. It is an excellent way to use practical knowledge in data-driven research. It enables several data sets to be evaluated during a regression test.

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