There are many methods for implementing application testing. However, not all require the same effort for test creation and maintenance. As the applic
There are many methods for implementing application testing. However, not all require the same effort for test creation and maintenance. As the application grows, so does the number of tests. It becomes hard to read and maintain a large number of tests, This eventually opens the path to defective code. With data-driven tests, you can ensure that the code works smoothly. If you need to run the same tests, with different parameter values, then data-driven testing (DDT) helps you do that.
Imagine a scenario where you have to automate a test for an application with multiple input fields. Hardcoding those inputs and executing the test become bulky, and confusing. Also harder to manage to run them through many variations of acceptable input values like for best-case, worst-case, positive, and negative test scenarios.
It would be easier if you could have all those test input data stored or documented in a single spreadsheet so that you can program the test to read the input values from it. Data-driven testing comes out as the solution to achieve this.
When you are automating a test for an application with multiple input fields, data-driven testing helps to achieve all the test input data by storing or documenting it in a single spreadsheet so that you can program the test to “read” the input values from it.
In this article, we will learn about the top trends in data-driven testing. To cover our topic it is important to know what data-driven testing is, why it is important, and its benefits and limitations. So let’s start with what data-driven testing means.
Data-driven testing is also called table-driven testing or parameterized testing.
It is the method of testing where you can take a test, either stored in a table or spreadsheet format and run it with as many different inputs as you like. In short, it means parameterizing the test and running the same test case with varying data, and getting better coverage from a single test. Additionally, with increased test coverage, data-driven testing enables to build both positive and negative test cases into a single test.
But data-driven testing capabilities allow for creating the test case once and reading the data from a spreadsheet and feeding it into the application. This way new test cases can be added to the data source, like this Excel file, or an XML file as needed.
Data-driven testing is used to extend automated test cases. Where the same test case can be run with as many different inputs as required, thus getting better coverage from a single test.
Data-driven testing saves a lot of time and money for a development team because it allows them to automate the testing process. Thus the team doesn’t need to test each piece of data manually. It also provides the ability to change the parameters of the test case and reuse them as many times as needed in other instances.
DDT separates test logic (script) from test data (input values), making both easier to create, edit, use, and manage at scale. Therefore, DDT is a testing approach where a sequence of test steps structured in test scripts are automated to run different permutations of data repeatedly to assess actual and expected results for validations.
Steps followed in Data Driven Testing
Parameterized testing involves a four-step process. They are
- Getting input data from the data sources XML files or other databases.
- Inputting data into the AUT (application under test) with the help of automated test scripts and variables.
- Evaluating the actual results with the expected output.
- Executing the same test again with the subsequent row of data from the same source.
Data Driven Framework
Data Driven Framework is an automated testing framework where input values are read from data files and get stored into variables in test scripts. It allows testers to build both positive and negative test cases into a single test. In a data-driven framework, the input data can be stored in single and multiple data sources like XML, CSV files, and databases.
Why Data-Driven Testing
You might be wondering why data-driven tests are so needed. There are so many reasons for that. Let’s have a look at them. Data-Driven Testing is a type of application testing method of creating test scripts and reading data from data files.
- Data Driven Testing is important because testers might often have multiple data sets that have to be used to test a feature of an application for a single test. Hence creating individual tests for each data set and running the same test with different sets of data manually is a time-consuming, error-prone, and laborious task.
- They reduce the cost of adding new tests and changing them when your organization’s rule changes. It is by creating parameters for different scenarios, and using data sets that the same test scripts can be executed for various combinations of input test data, and test results can be generated efficiently.
- This testing makes it easy to find out which data is most important for the tested behavior. Also, remember how code works when you need to change it.
- Data-driven testing helps in keeping the data separate from the test scripts and the same test scripts can be executed for different combinations of input test data and test results can be generated efficiently.
Benefits of using Data Driven Framework
The ability to parametrize testing provides extreme benefits important at scale. They are.
- In data-driven testing all the information is documented hence you can generate test scripts with less code, improve test coverage and reduce excessive duplication of test scripts.
- It allows the reusability of code. Therefore there is no need to modify the test cases over and over again for different sets of test input data.
- Allows to save test data and verification data in just one file, and creates a clear and logical separation of the test scripts from the test data. Therefore any changes in the test script do not affect the test data.
- Running the same test without creating a different test for each data set value saves a lot of time, and requires less maintenance. Hence, it allows the testers’ to spend their time on more valuable tasks and employ a more exploratory approach where needed. This as a result increases the flexibility of application maintenance.
- This testing can be performed at any phase of the development that allows for better error handling.
- different tools are available that can generate test data automatically and cover a large volume of test data to save time when necessary.
- All the information like inputs, outputs, and expected results is managed appropriately and stored in the form of text records.
Limitations of Data-Driven Testing
DDT enables scaling, but there are some boundaries to this method. They are
- Testers must need to have great proficiency in scripting language because the quality of the application depends largely on automation team skills
- Requires a large number of data files for each test case with many inputs. Therefore it needs more time to execute and validate the data.
- Creating a new test case demands a new driver script with different data so that the changes made to the test case should return to the driver script or vice versa
- Another big issue is the difficulty in maintaining and understanding code complexity and logic.
Top trends in data-driven testing
With the adoption of newly upgraded tools and trends, application testing organizations across the world have massively changed. Now they are focusing on digital transformation to be on the top spot and uplifted the quality of their applications. Therefore, organizations must carry out different types of testing. For this reason, the testers need to keep themselves updated with the latest testing trends to ensure that the application meets high-quality standards.
Many organizations are adopting data-driven models to make their testing processes more efficient. Hence they are inclining more toward the data-driven method due to its humongous growth. Data-driven trends can help an organization deal with many changes and doubts.
So, let’s take a look at a few of these data-driven trends that are becoming an integral part of the organizations which need to be followed.
Big data testing trend
Data is essential to any organization’s success or failure. And with the growing number of the latest technology organizations are now working on data over different volumes. And, dealing with such a vast amount of data requires proper attention and needs end-to-end testing to avoid any failure. Big data testing techniques offer accuracy and reliability in many aspects. It also helps in data-driven decisions and improves the organization’s strategies
Big data means a larger dataset that cannot be processed using traditional techniques. To test such big datasets, organizations utilize various large-scale tools, techniques, and frameworks. It includes data quality testing, performance testing, and functional testing of structured and unstructured data.
IoT means the Internet Of Things. Among the latest trends in the application, the testing organization is IoT testing which is growing rapidly in popularity. The main goal of IoT testing is to ensure the safe transmission of data over the internet.
Researchers suggest that these devices are leading the market and are going to continue in the future also. IoT is used in sensitive data like personal health data that need to be protected well before circulating through the internet channels. That is why the first thing that comes to mind for testers and developers is security. They are now coming up with different ideas and trends where their main focus remains on safety.
IoT testing checks the performance, functionality, and security of IoT devices. Some common types of testing in IoT testing are usability testing, compatibility testing, data integrity testing, and reliability and scalability testing.
Codeless automated testing allows the flexibility to generate simple test case scenarios without writing a large number of code lines, regardless of the application’s type and size. This feature maximizes testing efficiency, reliability, ease of inspection, low learning curve, and stability across the application development lifecycle process, and saves valuable resources.
Codeless Automation tools have been built based on artificial intelligence technology that allows quick forming test cases and fulfilling the automated testing needs. This as a result saves time by automating the process of writing and running test scripts and also keeps up the resources free from handling other tasks.
Automated testing tools like Selenium is one of the most popular testing tools that provide developers with codeless automation, thus allowing them to focus more on building innovative applications.
One of the growing trends in application testing is QAOps, a combination of two processes DevOps and quality assurance into one. DevOps targets developing an application and combining IT operations with it, and the QA role comes in the final checkpoint ensuring that the application delivered is of high quality.
Combining together the QA into the DevOps process the newly integrated approach comes out which is called QAOps. QAOps aims at creating a new application testing process model and increasing the overall quality of the process.
Agile and DevOps usage
Many Organizations have adopted agile as a response to rapidly changing demands and DevOps as a response to the need for accuracy and speed.
DevOps incorporates practices, rules, processes, and tools that help to integrate development and operation activities to reduce the time from development to operations. It has become a widely accepted solution for organizations that are looking for ways to shorten the testing cycles from development to delivery and operation.
Adopting these two technologies has helped many organizations boost their benefits. This trend has gained much attention and seems that it is going to strengthen and continue in the coming years too.
Data-driven testing with LambdaTest
The data-driven testing approach is required when the application is data-oriented, and the data changes more often. If the application requires a lot of input data and it is dynamic, then data-driven testing is the best approach as data is separated from the test script. When automating the application tests you can reuse the automated flow, and just switch out the input. You just need to change the data files whenever you want to update the data.
But no matter how many test cases you write, testing will be incomplete if those test cases are not executed on real devices, multiple browsers, and platform combinations. By testing on a real device cloud like LambdaTest you get to run your tests under real user conditions for the greatest user experience by knowing how users are going to behave in real-world situations. This allows for improving overall test accuracy by highlighting the stoppage in the user experience.
LambdaTest makes data-driven testing easier by giving dedicated fixture support. It is a cross-browser compatibility testing platform that gives access to a cloud of 3000+ real devices, browsers, and operating systems combinations to perform manual testing and automated testing of websites or web applications With access to an online device farm, you can also test in parallel using LambdaTest scalable, and reliable cloud environment.
You Can write more blog About Technology