What Are The Best Practices For Validating And Verifying Data-Driven Test Results

Testing is essential in creating high-quality apps and meeting user’s requirements. However, the need for automation arises when testers and developers have to repeat the same task continuously. Doing the same task repeatedly creates boredom among testers which thereby impacts productivity. Therefore in terms of time, quality, and cost of the application delivery automation testing is crucial. Among various approaches to automation testing, data-driven testing is one of the simplest and most flexible methods.

Data-driven testing enables testers to create data-driven tests that can be executed multiple times with different data sets. This allows them to ensure that the application works as expected for various input values rather than with just one set of values. This way they provide larger coverage, speed up the release of high-quality applications, and improve user experience.

In this article, we will cover the best practices for verifying and validating the results of data-driven tests. But before moving to that let’s first discuss what data-driven testing is, how it works, what are the benefits of performing this testing, and what we mean when saying data-driven test results have been validated and verified

What Is Data-Driven Testing?

Data Driven Testing is a useful testing approach to rapidly and efficiently designing test cases in multiple variations with different sets of input data and expected results to provide larger coverage. It allows running automated tests that simulate user actions over an application with different input data.

Quite simply, data-driven testing is a testing methodology in which the same sequence of test steps is performed repeatedly using varying input data to get better coverage from a single test. In addition to increasing test coverage, it also allows the building of both positive and negative test cases into a single test. Let’s get deeper to understand the concept of data-driven testing.

How does data-driven testing work?

In scenarios where it is needed to automate a test for an application with multiple input fields, testers would typically hardcode those inputs before running the test. But hard coding every data in the scripts will fail at scale. This is because hard-coded scripts are cumbersome, confusing, inefficient, and difficult to manage while switching through numerous combinations of acceptable input values of best-case, worst-case, positive, and negative test scenarios.

Therefore to overcome the situation it is best to keep all test input data in a separate class file or external file formats like Excel, Word, text file, or even from the database tables. That is what data-driven testing specifically tries to achieve. By separating test data (input values) and test logic (script), DDT makes it easier to create, edit, use, and manage at scale.

Steps involved in data-driven testing

• Fetching the input data from data sources like Excel documents, an XML file, CSV, or other databases.
• Putting input data into the AUT (application under test) using automated test scripts and variables.
• Evaluating the actual results with the expected output.
• Executing the same test again with the next row of data (from the same source).

Benefits of Data-Driven Testing

• Often there are multiple data sets for a sequence of test steps, and creating individual test cases for each set of data is time-consuming and ineffective. Data Driven Testing helps to overcome this issue by rapidly running automated tests over an application with different input data and provides a lot of coverage to ensure the performance of an application.
• Reduce the risk of unnecessary duplication and redundancy of test cases and automated test scripts.
• Enhance business logistics and speed up the decision-making process by reducing risks, and increasing ease of accessing and sharing information with real-time analysis.
• Generate a large volume of test scripts with less code amount.
• Allows developers and testers to keep test cases or scripts separated from the test data. Since the test cases are separated from the data set, they can easily modify the test case of a particular functionality without making changes to the code.
• Makes it easy to understand, maintain, and manage the test cases as all information like inputs, outputs, and the expected results are stored in the form of appropriately managed text records.
• The same test cases can be used several times which helps to reduce test cases and scripts.
• A change in the test script does not affect the test data.

Now that we have understood data-driven testing let’s move on to discussing the best practices for validating and verifying data-driven test results. To start with that let’s quickly go over what validation and verification in data-driven test results mean.

Validating and verifying data-driven test results

Validation and verification are the two most common types of test analysis.

Verifying data-driven test results means determining the quality of the application which includes all the activities associated with releasing high-quality applications, i.e., testing, inspection, design analysis, specification analysis, etc. Verifying data-driven test results at the initial phase of the development helps in understanding the application more comprehensively so that it can be built as per the user’s specifications and needs. Verification also helps in reducing the occurrence of defects in the later stages of development, hence reducing the likelihood of application failure.

Validation is a process of checking whether the application’s functionality fulfills the user’s requirements. It is done at the end of the development process and takes place after verifications are completed. If some defects get missed in the verification process then during validation they can be caught as failures and corrective actions can be taken. Validation takes place during testing like feature, integration, system, load, compatibility, and stress testing.

Best practices to validate and verify data-driven test results

Data validation and verification are essential steps in making data-driven testing as effective as it can be. It ensures the quality and accuracy of the data, especially when used for decision-making, reporting, or analysis. Below is a list of some of the best practices for data validation and verification, to help testers drive the best results.

Defining test objectives clearly

Before designing and implementing the data-driven test cases, defining the test objectives and scope is vital. This includes describing the purpose of testing, defining the expected outcomes and criteria for success, and how to measure and report the test results.  Having clear test objectives will help in laying the foundation for designing effective data-driven test cases aligning with the test goals and enabling successful testing outcomes.

Clearly defining test objectives also helps to select the right tools, strategies, validation methods, and data sources necessary for comprehensive and efficient testing.

Choosing well-prepared data sources

The quality of the data-driven test results depends mainly on the data source quality. Reliable data sources are essential for conducting meaningful and accurate tests that reflect real-world scenarios. Therefore choosing the data sources that are relevant, accurate, and up-to-date for the test scenarios is crucial.

Additionally, it is equally important to ensure that the data sources are accessible, secure, and consistent throughout the test execution. Although various data sources, like databases, files, APIs, or web services, can be utilized, verifying their integrity and availability is essential before using them. Implementing this best practice can significantly enhance the quality and effectiveness of data-driven testing.

Driving dynamic assertions

Driving dynamic assertions that incorporate the pre-test values/scenarios into the most recent data is essential. Verifying data becomes critical during code revisions and new releases. At this time, it’s important to have automated scripts that can enhance these dynamic assertions, i.e., including what was already tested into the current test lines.

Testing both positive and negative data

Testing the positives is a requirement that everyone does, but testing the negatives is also equally crucial. The effectiveness of an application is measured by its ability to manage exceptions. These exceptions can occur due to a worst-case scenario that was occasionally reproduced in the system. Therefore designing an efficient system is important so that these exceptions can be well handled.

Designing robust test cases

The design of the test cases is crucial for effective data-driven testing. Therefore created test cases should be flexible, effective, reusable, and maintainable. Parameters, variables, and data tables can also be used to store and retrieve the input and output values for the test cases. Testers can also utilize assertions, checkpoints, and error handling to thoroughly verify the test results against the expected outcomes. Implementing these principles results in more comprehensive testing and better coverage of various scenarios using diverse data sets.

Validating the test results          

Validating the data-driven test results is a crucial process as it checks whether the test results go along with the defined test objectives and criteria.

Validating the test result at different levels, such as unit testing, integration testing, system testing, and user acceptance testing helps to ensure the validity, completeness, and accuracy of the test results.

Verifying and improving the test results

Another best practice in data-driven testing is to review and improve the test results. Analyzing the data-driven test results across different test environments, data sets, and test runs helps to identify any gaps, errors, or anomalies in the test data, test cases, or test execution. This as a result ensures that the test results are consistent and reproducible.

Documenting and communicating the process regularly

Documenting, communicating, and updating the data validation and verification process is an essential best practice for ensuring data-driven test result quality and accuracy. Doing this will help in setting standards, identifying errors, monitoring data quality, adapting to changes in data sources, requirements, or objectives, collaborating with stakeholders or team members for feedback, improving data quality and accuracy, reporting results, etc. The documentation and communication process requires tools such as data dictionaries, data quality audits, dashboards, improvement plans, or feedback mechanisms.

Use LambdaTest for Data-Driven Testing

Data-driven testing aims to provide the greatest user experience by knowing the user’s behavior and interaction in real-world situations. This testing approach is required when the application is data-oriented and testers have multiple data sets for a single test and create individual tests for each data. Data-driven testing is important because here data is separated from test scripts and the same test scripts can be reused for different combinations of input test data and test results can be generated efficiently.

However, as there are large amounts of data, maintaining and testing each piece of data manually is time-consuming. Hence to save testing time and make it cost effective, the development team utilizes AI-powered test orchestration and execution platforms like LambdaTest to design and execute data-driven test cases. Using this platform tester can change the parameters of the test case to execute them as many times as needed on a wide range of real devices, multiple browsers, and platform combinations. In case any inconsistencies occur this platform allows highlighting the stoppage in the user experience, hence improving overall test accuracy.

LambdaTest is an AI-powered test orchestration and execution platform that allows running both manual and automated tests at scale. The platform enables testers to perform real-time and automation testing of websites and web applications across more than 3000 environments, real mobile devices, and browsers available online.

By automating data-driven testing using LambdaTest, testers can improve their test cases for more efficient execution. This shortens timelines, makes their task easier, and results in more thoroughly tested and better quality applications.

Conclusion

In conclusion, it can be said that data-driven testing is a powerful technique in the testing world, which allows for improving the efficiency and effectiveness of test automation.

By following the best practices mentioned above testers can achieve higher test coverage, reusability, and scalability by effectively designing, executing data-driven test scripts, and providing valuable insights into the application under test. Its ability to effortlessly handle various data sets empowers testers to uncover defects efficiently and deliver high-quality applications.