Before focusing on implementing data-driven testing in QA automation, let us briefly understand the two main keywords we need to revolve around. That is Data-driven testing and QA automation. Data-driven testing is a highly used approach by the software development team. This is important in delivering the correct and quality software application to the end user within the desired period. The multiple test data input required to test the software application is stored in a table or spreadsheet. Also, the tester uses a single test script that can execute all the inputs from the table and provides the corresponding output in the same table or spreadsheet. Thus, data-driven testing ensures the reusability and maintainability of the test resources.
Next, QA automation simply refers to software testing tools that can perform automated software testing with a minimum involvement of humans. It checks on the continuous testing of the software applications. This further helps in increasing the efficiency of the software applications. Thus ensuring a seamless software application.
Integrating Data-driven testing and QA automation has played a tremendous role in developing new-era software applications. In this evolving technology era, there is a requirement for the fast delivery of software applications and the assurance that these software applications are according to the user requirements and fulfill quality criteria.
Along with implementing data-driven testing in QA automation, we will learn about its benefits and key components. Also, we will look at various challenges that arise while implementing data-driven testing in test automation, along with the various best practices that can be taken to overcome them.
Data-driven testing saves the time and effort of QA professionals and provides them with several other benefits. Here is a list of a few of them.
- Data-driven testing is popular due to its capability to test multiple inputs. And then store their corresponding output in a single spreadsheet or table. Here, a wide range of data is being tested with a single test script, which further ensures the compatibility of the software application with whichever types of input the end user will enter. Thus making it the best way to improve overall test coverage.
- Data-driven testing in QA implementation offers code reusability. You need not copy the entire code for executing various test scenarios. Instead, you just have to store all the relevant test data in a single table and integrate it with the test script. The single test script will test all the data and store their corresponding outputs in the same source.
- Data-driven testing allows testers and developers to reuse the same test script for multiple scenarios. This means testers and developers have limited test scripts that can cover all the test scenarios. So, whenever an updation or modification is required, they can focus on limited test scripts and maintain them efficiently.
- The data-driven testing offers an efficient utilization of resources. Your time and effort are saved as you do not need to write multiple test cases. Also, the resources used for designing and developing numerous test cases based on the test case scenarios are optimized.
- We are well aware that humans are prone to error. But when we execute data-driven testing in QA automation, human interference is minimized, and accurate test results across multiple data sets are provided.
For the execution of data-driven testing in QA implementation, here is a list of components you should have.
- Test Data: Data-driven testing is completely based on the data. Therefore, you should have a well-organized set of test data. The test data must be in a single file to fulfill the key characteristics of data-driven testing. Also, the data should be diverse. It must include positive and negative data covering the edge cases and the boundary values.
- Test Script: Another component of data-driven includes a test script. Thus, you should have a test script suitable for testing various inputs. Diverse inputs must be provided to the test script by modifying its parameters.
- Data Source: All the test data are stored in a single file for data-driven testing. These files could be a spreadsheet, a database, or any other external file. Thus, it is important to integrate the data source properly so that inputs can be managed. Also, data sources allow dynamic testing, which later fulfills the objective of data-driven testing.
- Test Framework Integration: Test framework integration is as important as other components of data-driven testing. This allows testers to perform data-driven testing effortlessly. Also, the management of test data becomes easier. Various frameworks, such as Selenium or Appium, have special features and functionalities specially designed for data-driven testing.
For the execution of data-driven testing, you need to have test data. The test data for data-driven testing are stored in a single file. Thus, you need a data source. You have various choices when it comes to the data sources. You have an Excel file, a JSON file, a CSV file, a database, or an XML file. You also have various APIs available on the cloud for data sources. Therefore, choosing your data source responsibly to suit the test scenario and data format is essential. Also, it is considered the first step to select a data source because various things, such as the library used for reading and writing data, will depend on the data source you have chosen.
Once you have decided on the data source for your test process. The next step suggests you design a data model. The data model here will represent various attributes and the data structure of the test data. Also, it is recommended to design a data model because data management becomes easier. It helps in the organization of data. Data accessing is done consistently and logically. Various approaches include using classes, interfaces, or annotations to design a data model. You can select the one that best suits you.
The next step recommends you create a data provider. The data provider is a bridge between the data sources and the test cases. Since the data source is a file, it must be integrated into the test cases. To create a data provider, you may simply design a class with a method that reads data from the file. Then, the data is returned as an array or object to the test case. For an effortless creation of data providers, you can use available test automation tools and frameworks. Also, certain frameworks, such as TestNG, provide you with the annotation to mark the method as a data provider. This makes working with the data provider easier for other team members. One such tool that can be used is LambdaTest.
LambdaTest is an AI-powered test orchestration and execution platform to run manual and automated tests at scale. The platform allows you to perform real-time and automation testing across 3000+ environments and real mobile devices.
By seamlessly integrating with LambdaTest, testing teams benefit from a powerful solution that not only simplifies data provider management but also offers a comprehensive testing ecosystem for diverse environments and devices, contributing to the overall efficiency and effectiveness of the testing process.
You have a data source, a data provider, and a well-designed data model. So, the next step involves scripting the test case. The test case will take the data from the data source with the help of the data provider. The data will then be used to test and verify the software applications. You can use the test automation tools to ease up the process of writing the test case. They offer you various languages and syntaxes to write the test case. So, you can opt for the one that best suits your software application. Also, it has a feature through which you can add annotations to your test cases, making understanding easier.
The next and final step comes into existence once you have finished writing the test case script. That is to run and report the test. Test execution can be done either manually or using an automated tool. However, test automation is highly preferred by testers due to its effortless and efficient testing. Also, manual testing is more likely to give more errors as humans are prone to errors.
Once the test has been executed. You need to analyze and report the test. The test report should include whether the test has passed or not. If the test has failed, the report should also include the issue due to which the test has failed.
- The test data must be regularly updated.
- New data must be added to the existing data set. This will increase testing efficiency.
- Set up version control. This will help in keeping a log for test data.
- Regularly analyze the test script. Then, maintain and update it.
- Use exception handling in your test script.
We have the best understanding of data-driven testing. Why is it preferred while testing the software application? What are its key components? And how to implement data-driven testing in QA Implementation. Along with all this, we have learned about the best practices.
Using those mentioned above, we can easily perform data-driven testing. Data-driven testing will further lead to reusability. It also helps in maintenance. Data-driven testing thus leads to effective and efficient testing.
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