Quality plays a critical role in the success of any product. As the customer needs a product with the highest quality, it is the company’s responsibility to deliver it in a best-engineered way. However, such responsibility is shared and merely the sole responsibility of one team. It is a collective and collaborative process. Furthermore, if there is no strong communication base, companies find it hard to come by. As a result, it impacts the product quality. Agile solves this problem effectively.
Agile helps to align customer needs and product development properly. With its multiple iterations, it assures the best quality of the deliverables. In this journey, testing plays a key role. Testing is a part of the SDLC process that guarantees quality. Testing may be of many types and depends on the application type. However, testing applications through data has remained the most widely accepted choice, which in other ways is known as data-driven testing.
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What is a data-driven testing framework?
Let’s take an example. In a complex application to test a workflow with multiple input points, a tester needs to enter input in a permutation of numbers and execute the application to realize that it might fail for a different permutation of numbers. Maintaining this huge number of test data and results is no less than a pain! But if we could store data with a storage facility, it would save a lot of time which can be used to create the test cases. This is what exactly a data-driven testing framework does.
The concept of data-driven testing centered around test data and separating it from the test script. Though it sounds simple, the task is not that much more straightforward. In this process, a tester first documents all the input data in a storage file that could be of any format like .csv, .xls, XML, etc., and also covers all possible test scenarios. Secondly, a test script is developed where the input values are used to run all data sets iteratively. Data-Driven testing, therefore, conceptualizes an automated framework where testing is triggered on a set of data stored in a storage facility (files, databases).
In this process, there are four important steps:
- Different sets of test data are stored in a storage facility like a file or a database
- Test script is created to read this data, passing it through the required layers of the script. This automatically triggers simulation of other action items
- Retrieved results are stored and compared with what is expected and what actually is obtained.
- Continued testing with the next set of input data
Best practices of Data-Driven testing:
Below given are Best testing practices for Data-Driven testing:
- During the data-driven testing process, it gives the scope to use a realistic data approach.
- Inside the test script, test flow navigation should be appropriately coded.
- Use Data to Drive Dynamic Assertions
- Drive virtual APIs with meaningful data
- Positive as well as negative test cases must be executed.
- Reuse Data-Driven Functional Tests for Performance and security testing.
Advantages of Data-Driven testing
Data-Driven offers many advantages. Some of them are:
1. As you get multiple sets of data values for testing, the testing becomes quite effective, especially in the case of Regression testing
2. You can organize test data and verification data in just one file. Additionally, it is kept separated from the test case logic.
3. Based on the tool, it is possible to have the test scripts in a single repository. This makes the texts easy to understand, maintain and manage.
4. You can reuse actions and functions in different tests.
5. Some tools generate test data automatically. This is useful when large volumes of random test data are necessary, saving time.
6. Data-driven testing can perform any phase of the development. A data-driven test cares are generally merged in a single process. However, it can be used in multiple test cases.
7. Allows developers and testers to have a clear separation for the logic of their test cases/scripts from the test data.
8. The same test cases can be executed several times, which reduces test cases and scripts.
9. Any changes in the test script do not affect the test data
Disadvantages of Data-Driven testing:
Some Drawbacks of the Data-Driven Automation Testing method are:
1. Quality of the test depends on the automation skills of the Implementing team
2. For a large amount of data, data validation is a time-consuming task.
3. a large amount of coding is required for data-driven testing, which brings an additional maintenance burden.
4. High-level technical skills are required. A tester may have to learn an entirely new scripting language.
5. A text editor like Notepad is necessary to create and maintain data files.
6. There will be more documentation. Mostly related to script management tests infrastructure and testing results.
Types Of Data-Driven Testing
As you already know, Data-driven testing uses classes of iterative data to cover portions of the application. The method is straightforward, where the test scripts (programmed in a scripting language) are executed for test data stored in a test file.
At the core of this is the way the scripting language supports the data file. This is where DDT varies slightly; i.e., based on the types of data files it uses.
Some of them are as follows –
· Comma-separated values (CSV) files
· Excel sheets
· Database tables
· Script arrays
· Table variables