At present, we are faced with the reality that businesses are moving fast. That means that software development teams need to adopt strategies that enable them to move even faster. What’s more, there has been the emergence of new software development models, including the agile development process. This avails organizations of powerful tools that are able to respond to events as quickly as possible. Even further, these businesses have evolved through collaboration between self-organizing and cross-functional teams. For organizations to make the most of said agile processes, they require more effective and efficient testing strategies. However, the reality is that most development, testing, and quality assurance teams struggle to create and maintain their required test data. Additionally, said teams end up lacking confidence when it comes to testing data automation and data usage within the testing discipline. That’s where test data strategies come in.
Simply put, test data strategies encompass all the processes of creating realistic test data for non-production purposes. For example, development, testing, training, or quality analysis. Think of test data strategy as a design pattern for testing. Often, this includes the combination of code, procedure, and infrastructure that affects how the tests interact with data to stimulate what’s being tested.
Test Data Strategies
There are three major test data strategies that experts point to as having two main components: the creational piece and the cleanup piece. The former is all about how and when the data was created. The cleanup piece has to do with the methods used to set the data source back to its previous state.
The Elementary Approach
Right out of the gate it is prudent to mention that the elementary approach lacks the creational strategy. This is because the test automation code ends up not creating any data used within the test. Similarly, the approach doesn’t clean up any data after each test case runs. As such, it essentially lacks the cleanup component.
What’s more, you need to be aware that this approach will likely not work in most environments. However, it does come in handy as it then forms the foundation for other patterns.
Early on, while working with the elementary approach, you quickly realize that you must manage the data in the system if you are to get the results you want. For example, if the data within the test system changes because someone within the team changed it, you are likely to end up with a failed test. Additionally, you cannot simply change the data in the system and verify the said change and expect a successful re-run. Further, if you were to run the same test case in parallel, chances are you’ll end up with a race condition. One of these would pass while the other would fail. Overall, if your organization values consistent test results, the elementary approach will simply not suffice.
Refresh Your Data Source
A go-to solution for this problem is resetting the data source used within the application prior to executing the test. In between the different test runs, you want to reset the data source, thus ensuring that you have the same data with every test run.
The downside to this approach is that the consistent resetting of the data source ends up being quite costly. What’s more, refreshing said data source can be quite time-consuming and laborious. In most instances as well, very few testers have the technical know-how needed to implement a complete reset.
Similar to the elementary approach, refreshing your data source will only work with some test suites, applications, and environments. To ensure success, you want to understand your team’s constraints. This should inform how you align the entire approach with the goals for the tests you’ll be running. In retrospect, refreshing the data source might not necessarily play into today’s continuous delivery initiatives.
Selfish Data Generation Approach
Evidently, refreshing the database isn’t as feasible. A strategy that you could then consider is creating unique data for each test you intend to run. This is a build-up from the refresh data strategy that incorporated the creation component but failed to encompass a cleanup strategy.
Going into this approach, it would be best if you leaned towards a test case that creates the data it needs to verify functionality. The result is that you no longer have to encounter the race condition issue. Indeed, each test ends up with its own unique data whose functionality it can modify and verify accordingly. Besides, you essentially then eliminate the problem of long-running times that you would have otherwise encountered while refreshing the data source.
This approach is correctly tagged selfish data generation since the strategy only concerns itself with the tests at hand. The consequence is that you end up having all the tests run without having to encounter race conditions that result in false positives on the test reports.
Choosing the Right Test Data Strategies
As highlighted, the test data strategy is critical to the success or failure of the entire endeavor and, consequently, the rollout of the project you’ve invested in. The three test data management techniques mentioned above are the most common you’ll find in practice within most environments. Choosing the right test data strategy is your best shot at achieving real business value.
To arrive at which one is best for you, tart with a blank database. This should then be populated by known data. As the systems become more intertwined, you can use the test to create data using APIs or database queries. Overall, the premise appears to be that the data you work with should inform the entire test data strategy.
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