How are devs coding with AI?

Help us find out-take the survey.
Log in
Log in
Log in
Book a Demo
Get Started
616.2K 447.5K 7.6K
Back to Glossary

Data-Driven Testing

What is Data-Driven Testing?

Data-driven testing (DDT) is a software testing technique where test inputs and outputs are provided by an external data source. This approach separates the script of the test from its data so that testers can run many tests with one script but various sets of information. Using databases, spreadsheets, or XML files as sources for test data helps to increase the coverage of automated tests without much manual effort. This method is appropriate for validating applications in different scenarios without needing to rewrite or copy test scripts. It lessens the possibility of mistakes and makes work more productive. DDT works best when you want to check if an application functions correctly with many data sets. It ensures the app behaves correctly no matter what kind of input it deals with. This technique is commonly used in quality assurance processes, especially in settings where testing must be thorough across various inputs.

Data-Centric Testing

In data-centric testing, we pay special attention to checking the quality and correctness of data within systems dependent on or driven by data. These types of systems include databases and data warehouses. The main goal is to ensure that information that is stored and brought back is exact, uniform, whole, and safeguarded.

  • Data-centric testing focuses primarily on validating the database and data integrity, whereas data-driven testing uses external data (often from files or databases) to input values into test cases and verify the output against expected results.

Both are essential in varied software testing and development situations. They highlight the important role of data in present-day software systems.

The likeness of data-centric testing with data-driven development essentially stems from their shared dependence on the quality and trustworthiness of data. Data-driven development (DDD) refers to an approach in which choices made during software development, as well as the system design, are influenced by the analysis of information and its usage in real time. Similar to how it happens in data-centric testing, DDD highlights the crucial part that data plays in an application’s function. It verifies that the system is developed according to the characteristics of given data and knowledge derived from said information. Both methods highlight the significance of data in propelling processes and choices. This guarantees that systems are not just operational but also enhanced for precise and effective data management.

Implementing a Data-Driven Testing Framework

To make a data-driven testing framework, there are some key steps that you need to take so your testing process is efficient and can expand. This method emphasizes splitting the test script’s logic from its data for testing, making it simple for changes in one place and reuse across multiple test cases.

  • Choose a testing tool: Select a tool that works with data-driven testing and can be easily combined into your development environment.
  • Define the test data structure: Arrange your test data into a structured format like Excel, CSV files, or databases. This arrangement should be similar to reach and change data without affecting the test scripts.
  • Develop test scripts: Prepare scripts that can read and understand the test data actively. The scripts need to be flexible for different types of data inputs and outputs.
  • Integrate data sources: Connect the test scripts with your data sources, making certain that obtaining data is done efficiently and dependably so there are no problems with data bottlenecks.
  • Run and refine: Execute the tests using a framework. Keep an eye on test outcomes, making changes to data and scripts according to results. This helps improve the process of testing repetitively for better optimization of this cycle.

Using a data-driven automation framework can improve your test coverage. It also makes it simple for testers to include new data-driven automation testing scenarios as they define fresh sets of data, decreasing the need for maintenance. This method greatly enhances the strength and adaptability of your testing system, which brings us to the next segment.

[blog-subscribe]

Benefits of Data-Driven Testing

The use of data-driven testing (DDT) in software testing has significant benefits. It helps to check application functionality across various scenarios by using data sets, which increases the scope of tests and adds precision and efficiency to the process.

  • Enhanced test coverage: Data-driven testing, which automates tests with different inputs, makes sure that every possible combination of inputs is examined. This greatly decreases the chance for defects to go unnoticed.
  • Reusability of test cases: The test scripts for data-driven testing are written separately from the test data, which makes them capable of being used again in various datasets. This ability to reuse saves significant time and work because one script can check numerous sets of data.
  • Less maintenance: When test data is separated from the scripts, changing tests for new data scenarios does not require script changes. This division decreases maintenance work and makes updating tests easier.
  • Better accuracy and efficiency: In data-driven testing, the process of entering test data into the test cases is automated. This decreases human error and enhances the trustworthiness of tests. Simultaneously, it quickens the testing pace, enabling more frequent cycles in a given period.
  • Regression testing: Data-driven testing is perfect for places that need frequent regression tests. It can handle huge amounts of data, so it’s practical when there are a lot of things to check in the regression test after every change made to the code base.

Organizations can obtain dependable software deployments and enhance the excellence of their products by concentrating on strategies backed with data.

Conclusion

Data-driven testing (DDT) boosts the effectiveness and range of software testing by using different data sets. This guarantees comprehensive application functionality under diverse conditions. The method notably increases test coverage and makes it more precise by automating inputs, thus lowering the risk of defects being missed. DDT separates test data from scripts, permitting substantial reuse and simpler upkeep as alterations in the test scenarios do not require adjustments to script files. This method cuts down on manual mistakes and speeds up the testing procedure, allowing for faster results in every testing phase. These abilities make it very useful in guaranteeing the trustworthiness and excellence of software within a constantly changing technological setting. This is beneficial for organizations because it helps to maintain uniform and reliable software functioning.