Data is the foundation of the efficient work performed by technical products. Whichever apps you use, they perform all their operations on huge sets of data collected through different sources. And, those huge chunks of data are the reason why apps provide you with the best results.
You must also know that artificial intelligence, one of the most important aspects of the future of the tech world, works mainly based on extremely high usage of data. The primary base of AI’s work is letting the computer know about numerous possible conditions using huge data sets so that it can analyze different conditions and make decisions accordingly.
However, here we are going to focus on data-driven testing, which is a highly efficient testing approach that lets you perform multiple tests by using a single test script on multiple data sets. So, let’s move on with the article so that you get a clearer idea.
Data-driven testing is a software testing technique where a lot of test data is provided in a table or spreadsheet or any other format like an Excel document, MySQL database, an XML file, etc., and a single test script is used to perform the test using those test data. In this approach, the test output is also expected to be received in the same table.
This method can save you a lot of time by exempting you from manually testing each piece of data. And, on top of that, you can use advanced test automation tools like Preflight to automate all your tests so that you can sit back and relax while your tests will automatically get executed and you will receive detailed reports.
Data-driven testing also helps you to set up positive and negative test cases in a single test. That means a single test will analyze whether your input test data satisfies the specified boundaries. That means if the input data is in the correct format, it will turn out to be a positive test case, and if it does not satisfy its boundaries or is in an incorrect format, it will become a negative test case. This is a very important phenomenon for creating useful test cases.
From the description above, you can easily understand that data-driven testing is something that is useful for scenarios where the same test steps need to be performed repeatedly with different data. Usually, those tests are for checking the correctness of application logic. Here we have jotted down a few common logics that are tested with data-driven testing.
This is probably the most widely used logic nowadays. With the immense growth in eCommerce, it has become a huge necessity for the respective eCommerce websites to perform efficiently. That’s why you must also care about having properly working logic in the shopping carts.
Shopping carts have to deal with numerous items with huge valuations. In such a case, if the cart fails to perform proper operations, it will cost the eCommerce service provider huge losses. Hence, these become ideal use cases for data-driven tests.
eCommerce websites allow you to add various combinations of items from various categories. They also come up with special offers like “Buy 1 Get 1 Free”, and coupon codes are something that comes in various varieties and huge availability. Hence, each of these situations is a potential use case for performing data-driven tests on the shopping cart logic.
The internet has provided every product with the opportunity to reach out to the whole world. That means most of the popular websites are effectively usable all over the world. As a result of that global usability, those websites have to comply with all languages and cultural norms of multiple regions, and that creates a crucial use case for data-driven tests.
For example, with different guidelines of different regions, the format of writing dates varies throughout the world. Also, the formats of writing numbers, currencies, addresses, etc. are significantly different in different regions of the world. So, to ensure the best performance of the app across the world, you must make sure to perform rigorous data-driven tests on it.
Every app or website has a certain flow of steps to turn a visitor into a potential user. For that, each of the channelized steps must perform as it should along with guiding the person toward the next step. Also, there are scenarios where the app has to validate the correctness of the users’ input data. That means if the user enters valid data, only then the app should process toward the next step. Else, it should warn the user about the invalid input data.
Then comes the modification capabilities provided to the users. If any data is modified, all other data that is dependent on the modified data should automatically get updated. And, another possible situation to perform a data-driven test is checking the permissions that a user has. For example, while being able to perform some operations, a user might be restricted to perform some actions that he/she doesn’t have permission to. Hence, make sure to have effectively performing data-driven test cases for such situations.
You can decide whether you should perform data-driven testing or not based on the following guidelines/considerations.
If your response is yes to most of the above-mentioned questions, you have potential requirements for data-driven tests.
Well, though data-driven testing is a highly advantageous practice, there are some scenarios where performing it does not turn out to be a good decision. Let’s see some of such scenarios.
Although you know that data-driven testing is a great choice for the test cases that you have to repeat multiple times, there are tests that do not have that much frequency. So, if you are repeating a few tests occasionally, there is no point in wasting a lot of time and money on setting up data-driven testing for them.
Similarly, if you have the requirement for using a single set of data and occasionally varying it, then you can surely avoid performing data-driven testing there.
One prime condition of data-driven testing is that you are capable of specifying the expected outcome for each set of test data. Now, in some scenarios, the outcome varies tremendously, making it difficult to specify the expected outcomes. This can happen when different outcomes bring up different page views.
However, the point is that such insane complexity in the test results should guide you to avoid data-driven testing.
Besides these large reasons, there may be numerous more reasons to avoid data-driven testing. For example, if your testing team does not have the necessary skills to perform such an important form of testing, or if you are just beginning to test a new product and do not know whether performing data-driven tests here will be a good idea or not, or you are not noticing significant benefits of data-driven tests, you should not perform those tests there. There may be numerous reasons for not performing data-driven tests, and being responsible for a smooth testing experience for your product, you must make the assessments carefully.
Among the many advantages of data-driven testing, the most important ones are -
Besides amazing advantages, data-driven testing also has some disadvantages that are -
This article tells you a lot about data-driven testing so that you can get your ideas about it. If you carefully go through every point in this article, you can easily figure out the following conclusions -
You can clearly see that data-driven testing is overall a highly beneficial form of software testing but you must know that no form of testing can be favorably effective without efficiently automating them using advanced test automation tools like Preflight. This simple yet highly powerful browser extension lets anyone without any coding knowledge create, run, and manage complex test cases within seconds. To experience such ease in testing, all you need to do is get started with us.
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