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While there are a plethora of methodologies that can be implemented in software testing, for you to run tests with different parameter values with efficiency, data-driven testing (DDT) is your answer.

What is Data Driven Testing?

Data Driven Testing is a software testing method wherein test data is stored in a table or spreadsheet format such as CSV files, Excel files, datapools, etc. A single test script enables executing tests for all test data from a table and the test output in the same table, also known as table-driven testing or parameterized testing. This test runs multiple times, equal to the number of times we have the set of values in the data source.

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What is Data Driven Framework?

Data Driven Framework is an automation testing framework focused on separating the test scripts logic and the test data from each other, allowing users to create test automation scripts by passing different sets of test data.

Here are some of the tools / frameworks for data-driven automation testing :

  1. Selenium, TestNG, Apache POI
  2. QTP
  3. Testsigma
  4. Katalon Studio
  5. TestComplete

Example: How Data Driven Framework works in case of Log Ins-

For any application to be accessed, a basic login panel consists of an email and a password for security purposes. The data driven framework restricts unauthorized users from accessing data within a platform or product.

Noting these different variables in a storage file and calling these values individually in a functional test script is an efficient way of approaching this problem.

What is a data driven API?

Application Programming Interface (API) is a set of programming code that enables data transmission between one software product and another. APIs are used to access data from third parties to retrieve / exchange information, hides complexity and helps perform tests effectively and also provides security.


Overtime, The APIs have become vital in every organization, from midsize to businesses with global footprints. APIs are assets that connect systems, streamline workflows, making integration possible. In fact, beyond improving operational efficiency it has enabled cross-system communication.

What are the challenges of API driven data testing?

Initial Stage Of Setup

Manual testing helps confirm whether something works. Automated testing is essential along with APIs to gather information on how well they perform under pressure. Getting the testing infrastructure set up and actioned is sighted to be a major challenge – not because it is particularly difficult, but because it can be a substantial motivation-killer.

Maintaining the Schema

Schema is a data format that handles requests and responses for the API but requires maintenance throughout the testing process. Any updates to the program that give rise to additional parameters for the API calls need to be reflected in the schema configuration.

Testing Parameter Combinations

Communication between systems in API testing is managed by assigning data values to parameters and passing those parameters through data requests. It is mandatory to test all possible parameter request combinations in the API to test for problems pertaining to specific configurations. A larger chunk of data could end up assigning two different values to the same parameter, or could create numerical values where text values should appear. Adding an additional parameter exponentially increases the possibility of more combinations.

Sequencing the API Calls

When you add an endpoint to a URL and send a request to a server, this is referred to as an API call. Example : logging into any application or typing on a browser to get a desired outcome. API calls need to appear in a specific arrangement to work accurately, else it creates a sequencing challenge for the testing team. For example, if a call to return a user’s profile information goes through before the profile is made, the request will reflect an error.

Tracking System Integration

To ensure the API testing system is working coherently with the data tracking system. It is essential to bring back correct responses on whether a call is working without flaws. API testing is an integral part of application development in the modern business scenario.

How do you build a data-driven API test?

  • Making a list of the fixed sets of actions that the application should be performing against the API.
  • Collection of the data to put to test, putting it inside a table or spreadsheet, commonly referred to as “storage”.
  • Setting up a test logic with a fixed set of test data.
  • The fixed test data is to be replaced with a set of variable data.
  • Assigning the value from the data storage to the variables.

Best practices to make a note of in your data driven API tests:

1. Usage of Realistic Data
The test data with a detailed approach reflects the conditions that the API will encounter in production and therefore the test process is likely to be more comprehensive and accurate. It’s important to take into consideration that non-obvious interrelationships among data may prevail.

2. Testing The Outcomes
It’s important to confirm that sending incorrect or otherwise invalid parameters to the API triggers a negative outcome, which is commonly an error message or indicates a problem.

3. Use data to drive dynamic Assertions
Assertions allow you to validate HTTP responses in your API tests.They’re used to determine whether the API is behaving according to its specification, and are thus the primary metrics for quality.

4. Repurpose Data Driven Functional Tests for Performance and Security
Many organizations use an impractical approach, with narrowly focused performance and security tests that are also crippled by narrow sets of hard-coded test data. Reusing a data-driven functional test drives an aspect of reality to the performance and security evaluation processes.

Benefits of Data Driven Testing:

  • They reduce the cost of adding new tests and changing them, when business rules change. This is executed by creating parameters for different scenarios, using data sets that the same code can run against
  • Through data-driven testing, data can be separated from functional tests, which makes it possible to execute the same test script for different combinations. Hence test scripts can be generated with less code, since information is documented beforehand. It not only improves test coverage but also reduces unnecessary duplication of test scripts
  • As a result, testers can spend their valuable time and engage into a more intensive and analytic approach while increasing flexibility in application maintenance
  • Creation of extensive code is flexible and easy to maintain. It allows developers and testers separate the logic of their test cases/scripts from the test data
  • Enables execution of test cases multiple times which helps to reduce test cases and scripts

Drawbacks Of Data Driven Testing :

  • In a cycle where you are testing data continuously, the ‘right data set’ is hard to come by. Data validations are consume ample of time and the quality of such tests are dependent on the automation skills of the implementing teams
  • Although data driven testing (DDT) maintains separate test scripts and test data documents, the test code to understand and comprehend this data is slightly complicated. The programmer needs to be mindful of testing every data row in the data set/module.
  • For a tester, solving errors in DDT could be a tough task due to the lack of exposure he/she has on a programming language. There is a possibility to be unable to identify logical errors while a script runs and throws an exception. Sometimes there are changes of having to entirely learn a new language from the beginning. However, automated testing tools do not require intense programming skills


The growth of technology and its possibilities are infinite. Data-driven testing helps document and manage a large volume of necessary data. By using this approach, we are able to avoid the issue of running different test scripts for multiple data sets. Although it may have certain limitations, it does not have disadvantages. The dynamics of data-driven testing has a way forward to revolutionize the world of web services.
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