What is data-driven testing?
Data-driven testing (DDT), or parameterized testing, refers to the process of using automated tests in tandem with data sets to test applications against hundreds or thousands of inputs. The results of this testing are then compared with the expected results, to assess whether the application is performing to the requirements. The data that helps in enabling data-driven testing is referred to as test data.
A common use case of data-driven testing is checking the login functionality of a website or an application. Different test scenarios—with all the logical combinations of usernames and passwords, along with incorrect and empty data—is input, one by one, to analyze the outputs. The accuracy of the outputs, in accordance with the expected results data, can then assist in rectifying errors.
Test data preparation for data-driven testing
Test data is generally rows of data that are stored in .xl, .csv, or .xml files and databases, and the quality of this data affects the performance of data-driven testing to a great extent. Preparing test data involves all the different scenarios that the product might encounter during the production phase. For achieving maximum coverage, the datasets should be comprised of valid and invalid inputs, along with boundary conditions and edge cases.
Steps to follow for test data preparation:
Requirement identification
Assess and identify the data requirements for the tests.
Data collection
Procure or create the data required for the tests.
Data cleaning
Prepare the data for accessing within the test scripts.
Data management
Store and update the data to be referenced.
Data-driven testing framework
The data-driven testing framework automates the test scripts to use relevant test data for executing data-driven tests. The data-driven framework in Selenium WebDriver is a popular one that's used by QA teams for data-driven testing. Other notable examples are TestNG, JUnit, and pytest.
How data-driven testing helps QA teams
Data-driven testing can significantly benefit QA teams
Less time
The main benefit of data-driven testing is the reduction in time needed for testing. Automating the execution of the same set of test scripts with different input values requires minimal or no assistance from the QA team—and while data-driven tests are running in the background, testers can divert their attention to other important tasks.
Code reusability
Already existing test scripts can either be used directly or tailored to suit the requirements for data-driven testing. This flexibility allows testers to improve code quality for existing test scripts and also create better ones that can facilitate data-driven testing in the future.
Code isolation
Test scripts and test data are managed in different data sources or locations, to ensure that the test scripts remain unaffected and can be referenced as many times as needed, without influencing their effectiveness.
Improved bug identification
Generally, data-driven testing is pivotal in identifying certain bugs that aren't covered under other types of testing, due to the repetitive nature of checking all possible input combinations. Even if no bugs are uncovered during testing, the QA team can be assured that all possible scenarios are addressed.
Reduced human errors
Since automation is at the core of data-driven testing, all the errors associated with manual testing are non-existent once the test scripts are ready for automation.
Improved contributions
The preparation of test data and workflow implementation doesn't require much technical expertise, so anyone with minimal coding skills can also contribute towards data-driven testing as a way to utilize QA resources better.
Tips for efficient data-driven testing
When carried out efficiently, data-driven testing is a vital part of the software testing life cycle (STLC), especially at bigger organizations, where the applications involve a substantial amount of customer data and require extensive testing with a huge pool of data sets.
The best practices for getting the most out of data-driven testing are:
Dedicated file management
Maintaining separate folders for data sets, and test scripts with dedicated subfolders for cataloging them based on functionality, can help save QA teams a great deal of time which would otherwise be spent on optimization as the project progresses.
Good data quality
In many cases, the quality of data is the reason for not achieving maximum coverage of errors. This is why test data needs to account for all possible scenarios, including edge cases and positive/negative testing inputs.
Extensive automation
Test automation is the force that pushes data-driven testing forward, by executing tests on command, one after the other. While there are many options available for automating tests, choosing the best test automation solution can improve the productivity of QA teams.
Comprehensive reporting
As with any type of testing, detailed analysis of test executions is mandatory for efficient testing. In the case of data-driven testing, logging is essential for each test iteration.
Automated data-driven testing on Zoho QEngine
Zoho QEngine provides an intuitive and hassle-free way to test endless combinations of data, including edge cases and positive and negative cases. For data-driven testing, data sources, irrespective of the amount of data, can be easily tested with Zoho QEngine.
Performing data-driven testing on Zoho QEngine can easily be achieved with the following steps:
Create a data source file by adding individual rows of data based on different combinations, corresponding to the various scenarios required for optimal testing. This can be done inside the data source repository or by choosing the Data Driven Testing option when creating test cases.
Tweak the relevant test case to accommodate the parameters, which can be referenced using the @ symbol inside the test case.
Choose the essential test cases from within the test modules and select the environments needed for the test executions
After testing, analyze the results for the individual rows of data used, to understand where the test case passed and failed.
Summary
Data-driven testing involves developing a robust system to automate test scripts for testing the functionalities of an application. This use of automation and repurposing of existing test cases saves the QA team time, which can then be utilized for other important tasks. Automation also enables faster bug identification while largely eliminating the possibility of human errors. Data-driven testing is poised to reap even more benefits with the use of generative AI in the coming years.