Significance of Data Driven Automation Testing

Away from the world of manual testing and running through countless test cases, automation testing not only provides better but faster results. When your software has left the testing stage onto production, everything needs to be picture perfect. However, for the best results, even automation has to be done carefully since testing your software in every kind of use case might not necessarily lead to accurate results.

But, with data-driven automation testing, you are not only able to identify and consider the various variables and factors, but also improve accuracy for your testing variables.

Role of data-driven automation testing:

When testers have to run and design endless test cases and scripts for manual testing, using a data-driven approach can cut the time and effort significantly. With data on the framework and foundation of most processes in the IT industry, making use of data in automation testing is bound to perfect it too.

As data dependency grows by leaps and bounds across a variety of IT industry fields, making use of data-driven automation testing also guarantees great results. With a data-driven testing approach, you not only eliminate the time and effort required but increase the accuracy, productivity and cover all the major areas.

What is Data-driven Automation Testing?

By enabling testers to establish both positive and negative test cases with the help of a given input test data set, a data-driven automation testing script makes sure to run a large and targeted variety of tests. To simplify, it is more of a framework than a method to put your test data set as an Excel or table format into a singular test benchmark for comparing with other use cases.

Whereas in manual testing, you need to code and create countless use cases and test scripts to thoroughly check everything, automation simplifies it. With data-driven automation testing, it creates separate cases for both output, input and test cases. In automation testing, a data-driven framework keeps the input test data aside and evaluates it with the actual results in subsequent tests and use cases. This not only improves efficiency but also makes sure that you do not spend time making and testing use cases where they might not even apply.

Advantages and Significance of Data-driven automation testing:

By taking a more simplified and targeted testing approach, data driven automation testing introduces a lot of benefits. Its significance is increasing in the industry as companies depend more and more on data rather than a manual approach.

1. Different test cases and test data: As mentioned above, a data-driven automation testing approach separates the input data and final parameters. So, there is no longer the need to test scripts and cases over and again with a manual approach. A data-driven approach stores the script from previous tests and runs it to work on the subsequent ones without repetition and useless application of bad fitting cases.

2. Less Redundancy and reusability: The data-driven approach sets apart scripts from previous testing cases into a separate file and uses all of them to run by suitable methods. This means as a manual tester or using a manual approach, going over the test cases over and again is unnecessary. It eliminates the need to run so many tests by using its own data set to run a suitable framework and procedure of use cases. This also means that these cases are reusable and can be applied to other testing scenarios without a thought.

3. Analytics: With the involvement of automation and data dependence, it is also possible to analyze and use reports to create better-targeted use cases. With the test data script being used as necessary, you can evaluate what works best and what doesn’t. With such a targeted data-driven automation testing approach, it is possible to analyze potential defects, repetitions and missing elements much better than a manual approach.

4. Better efficiency and coverage: Because of the time saved as well as the reduction in redundancy, the code required to create an automated framework is also considerably less. It can also be viewed to an extent as an artificial intelligence application, but all the work is because of modeling of the raw data into actionable scripts and outputs. With regression testing models, your data-driven automation testing framework will target and identify areas that are not working as expected and help analyze errors too.

5. Good for big data and large test cases: If you have a big project or a lot of use cases to go through, data-driven automation testing is the way to go. This is because its raw data file is data scripts and sheets (can be very big), and the results, test scripts are equally exhaustive of any areas you need. Moreover, in the case of big data, it is the perfect application to work on a variety of test cases and give you insights into any large scale errors.

Final Words:

As all the data, both input and output are in the form of datasheets and tables, a use case analysis is necessary to figure out all the cases it can be applied to. You can usually expect the raw datasets to be in the form of XML, Excel spreadsheets and tables.

Working on creating a different test case for varying data and use cases is wasteful and with data-driven automation testing, so you can rely on an automated framework to do the work that would take several weeks or maybe even months in a short time.

As it is dependent on machine learning models such as regression techniques, it is best to go for structured and organized data that is best suited as per real-world examples. Using binary or fake examples is going to teach this to treat real-world cases the same way, which is something that you do not want.

A data analytics and machine learning approach is necessary for implementing such a framework to its full capability. With this potential, application and software testing are taking another leap towards a better and more productive future.

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