Data-driven Testing

Data-driven testing is a testing approach in which test cases are executed using data from an external data source, such as an Excel spreadsheet or a database. This approach allows for the automation of repetitive test scenarios with multiple sets of input data, making it more efficient and scalable than traditional manual testing.

Data-driven testing is a testing approach in which test cases are executed using data from an external data source, such as an Excel spreadsheet or a database. The basic process for data-driven testing can be broken down into the following steps:

• Test data preparation: The first step in data-driven testing is to prepare the test data. This may involve manually creating test data in an easily accessible and easy-to-update format, such as an Excel spreadsheet or a database. Alternatively, test data can be created using data migration tools or scripts, or by using a data generator tool.
• Test script creation: Next, test scripts are created that contain the instructions for executing the test case. These scripts are written in a programming language, and they are designed to read in test data from the external data source and use it as input to the test case.
• Test execution: The test scripts are then executed, and the test data is read in from the external data source. The test case is then executed using the input data, and the output is compared to the expected results.
• Test result validation: The test results are then validated by comparing the expected output with the actual output for each set of test data. Additionally, it's important to keep track of the test results and to use reporting and visualization tools to help you analyze the test results.
• Test maintenance: Finally, test data is maintained by keeping it in an easily accessible and easy-to-update format, such as an Excel spreadsheet or a database. It is also recommended to store test data in a centralized location and to version-control it to keep track of changes.

Overall, data-driven testing allows for the automation of repetitive test scenarios with multiple sets of input data, making it more efficient and scalable than traditional manual testing. Additionally, it helps to ensure the reliability and stability of the application under test by testing it against a variety of data inputs.

Data-driven testing is majorly used by Software developers, quality assurance engineers, and other members of a development team utilise data-driven testing to automate application testing. The purpose of data-driven testing is to ensure that an application works properly with a broad variety of input data. By automating these tests, teams may detect and resolve defects more quickly and effectively, enhance the quality of their software, and boost their confidence in the application's capacity to handle a variety of inputs.

Data-driven testing can be used for a wide range of software testing activities, such as functional testing, performance testing, and integration testing. It may not, however, be the optimal strategy for certain types of testing.

Data-driven testing, for example, is particularly well-suited for functional testing since it allows for the efficient testing of different sets of input data, which can aid in the identification of errors early in the development process. It may also be used to test multiple scenarios with different data inputs to examine how the system performs under various situations.

It's also beneficial for performance testing, where the purpose is to validate the system's performance with various inputs. Data-driven testing allows you to produce large sets of inputs and assess the system's performance in various circumstances.

Data-driven testing, on the other hand, may not be the optimal strategy for exploratory testing because it relies on manual testing without preset inputs.

Furthermore, data-driven testing may not be the optimal strategy for usability and accessibility testing because it primarily focuses on analyzing how people interact with the system and how they perceive it.

Furthermore, for security testing, data-driven testing may not be the optimal strategy; instead, specialist tools and procedures should be used.

Overall, data-driven testing may be used for many forms of software testing, but it is critical to understand the testing project's individual goals and limits and to select a suitable technique for each testing type.

Using Ui Inspector to set up data-driven testing might entail multiple steps:

• Create a test: First, in Ui Inspector, create a test that includes the test procedures and anticipated outcomes. Ui Inspector defines tests using a simple, natural language syntax, making it straightforward to construct tests without writing code.
• Construct a data source: Next, you must create a data source containing the test data. Ui Inspector supports a variety of data sources, including CSV, JSON, and Excel.
• Test data should be mapped to the test: Once you've established the data source, you'll need to map the test data to the test. You may use params in Ui Inspector to refer to test data within the test case. The test may then be conducted by executing the test script, which pulls in the test data from the external data source and utilises it as input for the test case. You may run your test locally or in the cloud, and you can view the results of each test case.
• Analyze the outcomes: Finally, you may compare the predicted output with the actual output for each set of test data to examine the test results. Furthermore, it is critical to maintain track of the test findings and to use reporting and visualisation tools to assist you in analysing the test results.
• Repeat: After the initial run, you may repeat the procedure and make changes to the test data, the test script, or test steps as needed.

Furthermore, Ui Inspector has a feature called "Smart locators" that makes it easier to discover the relevant element on a web page, even if the web page has been updated. This implies that even if elements on the web page change, Ui Inspector can detect the matching element and execute the test automatically.

Yes, you may use data-driven testing with your existing test automation framework. The method will vary depending on the framework and tools you are using, but here are some common steps you can take:

Prepare the test data: First, prepare the test data in a format that is easily accessible and easy to update, such as an Excel spreadsheet or a database.

Construct the test scripts: Next, use your existing test automation framework to create the test scripts. These scripts should be written in such a way that they can take test data from an external data source and use it as input to a test case.

You'll need to link the test data to the test script since you'll be using the data to run the test on the test script.

Run the tests: After you've set up the test data and script, you can run the tests by executing the test script and feeding it the test data.

Finally, confirm the test findings by comparing the predicted output to the actual output for each set of test data.

Repeat and improve: After the initial run, you may repeat the procedure and make changes to test data, the test script, or test steps as needed to enhance the test suit.