Testing is a process wherein we watch software in action and look for discrepancies between the product’s expected behavior and its actual behavior. In other words, testers look for breakthroughs, gaps, oversights, and defects in the system under test. Moreover, with the software testing, the testers have got data-driven testing that comes with a way to improve the code coverage. Data-driven testing allows engineers to run the same test over and over again with different data. The same automated tests can be run with varying data depending on inputs from the engineer, which allows for more test cases than would normally be possible given the amount of time it would take to write each test individually. This allows testers to get more complete coverage of their code base.
Moreover, you can use it to establish best practices in your team and enhance the communication between developers and testers, so everyone understands better what they’re responsible for and how they can improve the quality of the product, while still keeping the overall costs under control and even reducing them, since the time-consuming manual regression testing will be a thing of the past.
Introduction to Data-Driven Testing
One of the problems with traditional test cases is that they often require manual effort. If a person needs to create the test case, execute it and debug it, it can be difficult for test cases to be optimized for specific processes. Data-driven testing seeks to optimize test cases so that functional testing is done quicker and more thoroughly by using external data from which automated test flows can be created.
- Testing code can be as valuable as the code being tested. As software development cycles have become shorter and more efficient, so too must our QA tests follow suite. Data-driven testing excels when working with large amounts of data.
- For example, if you wanted to test how a webpage handled large data, you could create a massive spreadsheet with thousands of rows and columns of unique data and use the power of data-driven testing algorithms that are compatible.
- This is different from typical coded tests because in typical coded tests all your variables exist in one column and therefore, at best, you can write out specific conditions for testing or entire scenarios using one specialized set of input values. Instead, with external data, you have the power to define any number of conditions.
Precisely, data-driven testing is an approach to software testing where instead of selecting the next test cases from a predefined list, you select them based on the data used by the functional tests. This means that functional tests can be executed over and over again using different data, thus increasing coverage.
Advantages of using Data-Driven Testing
Data Driven Testing is a methodology that promises massive reusability. It is essential to software development and continuous testing, especially when you need to scale up. A feedback loop enables you to be proactive and break the barriers of traditional methodologies. Moreover, it is an emergent collection of technology standards in cloud computing, with emphasis on distributed data processing – no SQL databases, REST web services, message queues, data warehouses, streaming analytics, and offline analytics.
- The biggest advantage of using data-driven testing is regression testing that is associated with it. Moreover, the basic aim behind regression testing is to re-test the existing features of a system, every time a modification is made to the code. This helps ensure that the modification made hasn’t broken any of the existing functionality of the software/ application, thus ensuring that it continues to work as expected. Furthermore, data-driven testing is an approach that can help to streamline your regression testing and enhance software quality.
- In test management, we need to confirm that a product has been tested well. This can be achieved by code coverage analysis, fault injection, and automated testing. Code coverage analysis verifies that every line of code has been executed. Fault injection makes sure that every critical aspect of control flow within the code has been exercised and that every situation-response pair in the code has been exercised with all specific correct inputs, so it is considered as a subset of the functionality verification. Automated testing ensures that no defects are present in a software application. These activities help assure the quality of the product and hence can lead to confidence in its smooth operation after release.
Why use Data Driven Testing?
Data-Driven Testing can be used in the design phase to determine whether a feature is right-sized for the requirements and whether it satisfies those requirements. Once a feature is built and presented to users, DDT helps conduct user studies to see if the assumptions made are correct. If they’re not, again, DDT is used to re-think, re-imagine and re-make based on what you have learned from your users.
- It is necessary to test a system for both the positives and the negatives. This means that you should determine if there are any possible situations in which something could go wrong and test to ensure that if this occurs, your system will still function properly. Furthermore, this helps us to understand that either it will be an example of how a system deals with failure, or will it show that certain stimuli are outside the expected operating range.
- There are so many different kinds of testing possibilities in software development today: unit, integration, functional, security, performance, end-user acceptance, and even exploratory. No one can write all those tests by hand. However, with the data-driven testing methods, all the codes present along with the tools can be easily tested.
- Most of the time teams use manual interventions to trigger an automated workflow. This shouldn’t happen at all. If a deployment process has multiple navigational or re-directional paths, it’s better to create a test script that can accommodate all these paths instead of doing the deployment through the command prompt. This is because a human can never replace an automated process in testing a flow. It’s always best to make sure test scripts contain their own navigation and not rely on others to trigger them.
- The major benefit of running the performance test before and after you add a new module or feature on the network is not only having the right amount of information about how it fits into the system but also checking if it has been designed fluidly and with security in mind. Since you are planning to measure the processing power, response time, and capacity of a function, go further and check also how it fits into security parameters. When testing your design, look out for malicious attempts that could harm it from a functional reliability perspective.
Working of Data-Driven Testing
Data Driven Testing is a software testing method, which is employed to uncover defects in a piece of software. This technique is also used to ensure or verify the quality of such software. It works by using strings of text that are converted into data sets to be used as input at runtime to record the operation of a test case in its journey through the application being tested.
- The driver script makes automated test tools work. You’ll need a variety of data sources to run automated tests. At least, the automated test tool should be able to read text files and read from the internet, but most automation tools are capable of much more. Driver scripts make your script useful by supplying test inputs so that you don’t need to type them into the tool yourself. And with it, the test data is created for the user.
- The test data is a set of predefined input scenarios and expected output scenarios based on the type of software under test. One can draw on the table or canvas using any data value. Scalar data, in the form of strings or numbers, can be assigned to variables in tests, and a matrix may also be created from a table containing multiple rows and columns of data. This is called the test data structure. Test data is used during test execution to create a formal representation of test cases, which is called Test Inputs for the use of Test Steps
Efficient Management Of Test Data throughout the course of a project’s execution cycle, an out-of-control test data management system could prove to be catastrophic. As a tester, one of your primary roles is to manage the test data for each scenario and make changes when necessary to ensure its integrity. You may not be able to determine exactly how or why your test data became corrupted. However, it’s imperative that you know the actions to take in order to address this problem before it becomes worse.
Final Thoughts
Within recent years, the data-driven testing world has been changing quickly. The basics are still the same: Test cases still run against feature-rich software to ensure business objectives are met as well as bugs are caught. But there have been multiple changes in how and when testing occurs. Long gone are the days of manually creating, altering, and versioning scripts for even the most lightweight test runs. Nowadays, engineers can test their code continuously by using powerful continuous integration tools that can automatically run tests using different input scenarios. In a way, they’re becoming more like automated developers than testers or manual QAs. The practice of advanced testing techniques is also changing rapidly, thanks to data-driven testing and software-as-a-service providers offering a pre-made platform of this type and more.
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