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What is Data-Driven Testing? Learn to Create a Framework
Data-Driven Testing
Data Driven Testing is a type of software testing wherein testing data is maintained in table or spreadsheet form. Data driven testing enables testers to provide a singular test script that may perform tests for all test data from a table and expects the test results to be returned in the very same table. Table-driven testing is another name for parameterized testing.
Automated tests replay a documented (or planned) series of user activities covering a specific portion of the tested app. You may expand access by running tests with varied input information. Assume you logged activities that entered data into a form in an application. The documented exam covers just the data that you input during the recording, and these data are unlikely to create mistakes in the program, but some other information may. As a result, you must conduct your test using a diverse collection of data input to check that the application works as planned for a variety of input values. This method of testing is known as data-driven testing.
In fact, you could document a large number of tests with varying input data, but the optimum option is to segregate data from test commands. Rather of using hard-coded values, data-driven tests will receive data from data storages, such as a file or database. This split simplifies testing conceptually. A test with many sets of hard-coded values might be difficult to adapt and sustain. For example, if you want extra input data, you must adjust the test instructions.
Data Driven Framework
Data Driven Framework is an automated testing framework that reads data from file systems and stores them in variables in test scripts. It allows testers to combine positive and negative test scenarios into a single test. Data input in a data driven framework could be contained in a single or several data sources such as.xls,.xml,.csv, and databases.
This tutorial will teach you
- What exactly is Data Driven Testing?
- Why is Data Driven Testing used?
- How to Build a Data-Driven Automation Framework
- Data Driven Testing Best Practices −
- The Benefits of Data-Driven Testing
- Data Driven testing has the following drawbacks
Why is Data Driven Testing Used?
Data Driven Testing is critical since testers usually have several sets of data for just a specific test, and designing distinct tests for every data set can indeed be time intensive. Data driven testing allows data to be kept away from test scripts, and then the same test scripts may be performed for numerous combinations of input test data, resulting in improved test results generation.
Example − For instance, we would like to verify the login feature with 1000 distinct data sets and several input fields.
To put this to the test, try the following methods −
Approach 1 − Write 1000 scripts, one per dataset, and execute each test individually.
Approach 2 − Change the value in the test script by hand and execute it numerous times.
Approach 3 − Enter the data from the excel sheet. Obtain test data from Excel rows one at a time and run the script.
The first two possibilities in the list are hard and time-consuming. As a result, the third technique is preferable.
As a result, the third method is nothing more than a Data-Driven framework.
How to Build a Data-Driven Automation Framework?
Assume you wish to evaluate an application's login capability.
Step 1: Determine the Test Cases
Enter the correct username and password – Login is successful.
Wrong username and password entered — Login Failed
Incorrect password and valid username – Login Failed
Step 2: Produce accurate Steps for the last three test cases
| Test Case # | Description | Test Steps | Test Data | Expected Results |
|---|---|---|---|---|
| 1 | Valid credentials can be found by testing Login. |
| Username: valid password: valid | Login Success |
| 2 | Invalid credentials should be checked in Login. |
| Username:invalid password: valid | Login Fail |
| 3 | Invalid credentials should be checked in Login. |
| Username: valid password: invalid | Login Fail |
Step 3: Write a Test Script
If you look closely, you will see that the Test Steps Remain Common throughout the Three Test Steps. To carry out these tasks, you must develop a Test Script.
// This is known as Fake Code.
// Step 1 of the Test: Run the App
driver.get("Application URL");
// Step 2 of the Test: Input the Username
txtbox username.sendKeys("valid");
// Step 3 of the Test: Enter Your Password
txtbox password.sendKeys("invalid")
// Step 4 of the Test: Analyze the Results
If (Next Screen) print is successful, otherwise it is a failure.Step 4: Prepare Excel/CSV file containing the Input Test Data.
Step 5: Update the Script such that it loops through the given test data. The input instructions should be specified as well.
// Pseudo Code
Loop 3 Times
for (i = 0; i & lt; = 3; i++) {
// Import data from Excel and save it in 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
}These are only three examples of test cases. By attaching test data values to Excel, the test script could be used to cycle over the following test scenarios.
Invalid username and password entered — Login Failed
Incorrect username and password – Login Failed
Enter a blank username and password – Login Failed
And so forth…
Data Driven Testing Best Practices
The following are the best testing strategies for data-driven testing −
Using genuine data throughout the data-driven testing process is excellent.
The routing of the test flow must be coded within the test script.
Use useful data to power virtual APIs.
Data may be used to generate flexible assertions.
Both positive and negative test results are possible.
Data-Driven Functional Tests for Security and Reliability
Benefits of Data-Driven Testing
Data-Driven offers several benefits, some of which are as follows −
During regression testing, it is possible to test the application with several sets of data values.
Test data and verification data can be combined into a single file that is distinct from test case logic.
The test scripts might be kept in a central database depending on the tool. This makes the texts simple to comprehend, retain, and administer.
Activities and operations can be reused across many tests.
Some technologies create test data on their own. This is beneficial when huge amounts of random test data are required, as it saves time.
Data-driven testing can be used at any stage of development. Data-driven testing concerns are typically combined into a single procedure. It can, however, be utilized in several test cases.
Permits developers and testers to keep the logic of their test cases/scripts separate from the test data.
The same test cases may be performed several times, which aids in the reduction of test cases and scripts.
Modifications to the test script have no impact on the test data.
Drawbacks of Data-Driven Testing
The following are some disadvantages of the Data Driven Automation Testing procedure −
The test's quality is determined by the Applying team's automation skills.
When testing vast amounts of data, data validation is a time-consuming operation.
Maintenance is a significant challenge due to the massive amount of coding required for Data-Driven testing.
High-level technical expertise is necessary. A tester may be required to learn a completely new scripting language.
There will be further documentation. Script administration, testing infrastructure, and testing outcomes are the most common topics.
To generate and maintain data files, a text editor such as Notepad is necessary.
Summary
Data-driven is a test automation framework that saves test information in the form of a table or spread spreadsheet.
In the Data-driven test automation framework, input data can be backed up in a single or several data sources such as xls, XML, csv, and databases.
Creating a unique test for each data set is a time-consuming and labor-intensive task. This problem is solved by keeping data distinct from functional tests in the Data Driven Testing framework.
It is a good idea to utilize actual data in Data Driven Testing.
During regression testing, it involves testing the application with numerous sets of data values.
The disadvantage of this strategy is that it is dependent on the implementation tea's automation capabilities.
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