If you need to quickly and efficiently develop test cases in multiple variations based on different input data and expected results, data-driven testing is a useful testing method. A reliable software delivery process relies heavily on automation testing. A machine that runs tests 24 hours a day, seven days a week is more efficient than a manual tester, which means it can run a lot more tests than a human tester. However, if you fail to automate tests, it can be pretty inefficient. The purpose of data-driven testing is to reduce repetitiveness in automated tests made up of only minor changes to test data.
Before we get into some of the advantages of data-driven testing, let’s back up and review some basics.
Data-driven testing: what is it?
Automated tests often involve repeating the same test steps with different variations of data and results. For example, if you are testing a registration form’s address fields, you will need to be able to test a wide range of valid and invalid addresses if your application needs to work in multiple regions. After setting each region individually, checking that the correct fields are displayed, and then entering test data into each field, you will be able to verify if the system is working correctly.
Compared to test automation, data-driven testing is slightly different. In standard testing, data is included in the test itself. In data-driven testing, the data source is connected to your test. CSV files, XML, and even full-featured databases such as MySQL, are among the many available data sources.
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Choosing the right data source
It is possible to test your login with a set of usernames and passwords using a simple structured text or CSV file. A more complex test suite may require XML so that additional information can be added to the test data. If you want to orchestrate your tests, it is particularly useful when you want to use a relational database such as MySQL.It is possible to test your login with a set of usernames and passwords using a simple structured text or CSV file. A more complex test suite may require XML so that additional information can be added to the test data. If you want to orchestrate your tests, it is particularly useful when you want to use a relational database such as MySQL.
Data source connection
If you are writing a Selenium test in Python, you can easily link it to your test. When you have a data source, you need to link it to your test. If you use XML as a data source for more complex scenarios, you may want to include a step that imports your test data from a CSV file. Then you create a loop that loops through each entry in the data source. In this case, you can import it into your script and parse it to extract the data and the expected results. This becomes much more difficult if you need to use a database source.
The challenge of analyzing the result is one that is often complicated. Sometimes, there is only a binary choice, but sometimes there are several possible outcomes. To compare the actual outcome with the expected outcome, you could use a case statement. Test automation engineers will showcase their skill here if the outcome is more variable. If the outcome is variable, you might want to use XML to provide a richer description of what to expect.
7 benefits of data-driven testing
Data-driven automation testing frameworks and DDT tools can provide many benefits, but here are the top ones.
Reusability and clarity
Test scripts can be run with any number of test inputs, provided as test data, once they are ready. Since the script will read the test data from the file and execute the test case, there will be no need to make any changes to it. Hardcoding the test data into the test scripts, on the other hand, increases the number of lines of code and compromises the program’s reusable nature. When test data and scripts are separated, the maintenance and development of both are clear. If test data needs to be changed, the testing script can be modified without referring to the test data.
The test scripts and test data are separated so that there is no dependency between them when updates are required. Regardless of the test scripts being worked on by the automation test engineer, any team member with knowledge of AUT can maintain and update the test data file without any dependency on the test scripts.
Coverage of a wider range of tests
As a result of high-quality test data that perfectly matches the production data, we achieve very good test coverage, leading to improved quality and application stability. Since all the possible data has already been tested and issues have been addressed, there will be very few defects in the production environment.
Reduction of manual errors and saves time
A hefty amount of data is entered into the application manually, so human error is unavoidable. The test data is read from files rather than manually entered in the application during manual testing. Automation testing based on data reduces the chance of manual errors to a great extent, which results in a high-quality product. As a result, manual entry of test data is monotonous and time-consuming. Data-driven automation testing reduces redundant work and allows human skills to be used differently, such as exploratory testing.
In order to save a great deal of time in testing activities, the automated test run can be set to run every night when there is nobody working. Results can be verified in the morning, thus saving a great deal of time. Furthermore, data-driven automation testing is faster than manual testing because it incorporates all of the advantages of automation testing.
Reduction in the number of test scripts
It is possible to use the same script logic for a large set of test data because the data is not hard-coded in the script logic.
The ability to make decisions more quickly and develop application more independently
Because automation is faster and more data is executed more quickly, management and defect-related decisions can be reached more quickly. Data-driven automation testing aids Agile and DevOps intricacies to a great extent, as shorter development cycles require quick decisions and quick resolution of issues. Due to the fact that we will be dealing with parameterised variables, independent test data files and separate test scripts, the data-driven automation testing framework can be developed before or in parallel with the actual application development. In order to create test scripts to read/write test data from files and scripts for test functions, expertise in test scripting is required.
Test data creation requires a minimum level of technical knowledge
In the data-driven automation testing process, scripting and test data are separated. The scripting part requires technical expertise, but the test data generation does not require it. A person with knowledge of the AUT and domain would be able to fill in the test data values in the sheet/file.
Disadvantages of data-driven testing
There are some drawbacks to Data Driven Automation Testing:
- Implementing team’s automation skills determine the quality of the test
- A large amount of coding is necessary for Data-Driven testing, thus maintenance is a major concern.
- When testing large amounts of data, data validation is a time-consuming task.
- An entirely new scripting language may have to be learned by a tester.
- Creating and maintaining data files requires a text editor like Notepad.
- A greater amount of documentation will be available, mostly related to script management, testing infrastructure, and results of testing.
Data driven testing in Agile
The benefits of the agile test environment include the highest standard of quality for products designed to meet the needs of their clients, which means that many people have a stake in the quality of the product.
A single unit inside an organization is rarely in charge of this task; rather, a team effort should be involved. Unless there is efficient communication throughout the organization, which is hard to come by, this can be problematic. Ultimately, this issue impacts the product directly or indirectly. Agile solves this problem!
In agile development, iterative development is encouraged as well as periodic inspections to determine what needs to be changed and the readiness to adjust quickly to those changes. The philosophy promotes teamwork in laying out requirements and developing solutions using engineering best practices.
Data driven Agile is intended to add a layer of data to Agile processes, not replace Waterfall or Agile approaches. It adds a real-time, quantitative perspective on engineering output to existing Agile processes. Using Agile Data Driven methodologies, managers and executives can identify potential barriers, prepare reports faster, and deliver work more quickly.
To make sure your online and mobile applications deliver the best user experience, test them on real devices, operating systems, and browsers. A real device cloud rather than an internal device lab is a more cost-effective and preferable option for Data driven testing compared to setting up an internal testing infrastructure.
A cloud-based testing platform like LambdaTest can help meet your testing needs in this situation. With over 3000+ browsers available online, you can test your web applications and websites. LambdaTest offers online device farms for your mobile and web testing requirements. LambdaTest makes the cloud environment accessible and simple, making software testing scalable and controllable.
LambdaTest’s support test automation with frameworks like Selenium, Playwright, Puppeteer, Appium, Espresso and more.
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The term data-driven testing (DDT) describes a testing methodology where data is the driving force. The selection of test data becomes imperative in data-driven automation testing. Performing automated tests on a huge data set without a proper vision is a waste of time, effort, and resources. A good domain knowledge and a thorough understanding of the AUT prepare the background for great test data. There is no doubt that data is everything, and Big Data, AI (Artificial Intelligence), and ML (Machine Learning) further substantiate this truth.
As in all areas of life, testing cannot be any different, so it is essential to select test data with care, so that any potential defects are already caught in testing and the risk of any potential defects in production is minimized.