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Test Data Management for Continuous Testing
Perform reliable testing with reusable synthetic test data.
Test Data Management
What Is Test Data Management?
Developers use test data to validate and optimize application functionality, including common or critical user paths and corner cases. When they use realistic test data, it also helps to replicate error conditions and reproduce defects.
Application APIs must facilitate smooth electronic data exchange in real time while interacting with APIs from multiple vendors and partners. When performing integration and API testing, DevOps and QA teams often spend an inordinate amount of time waiting for test data from data sources. This causes delays that can impact the sprint or the entire software delivery.
To prevent your application testing from stalling and to maintain referential integrity, you need access to realistic test data on demand. Test data management (TDM) provides a way to create and manage safe and appropriate datasets that your company can use across multiple teams for validating an application’s functionality.
Virtual test data clears the path for DevOps teams to achieve continuous testing. Test data management monitors actual traffic and data patterns to generate data models from the interactions in your system and automatically infers information about the data warehouse to make it easier for non-technical users to get the test data they need.
By using synthetic data with your virtual services, you can test for a wide variety of conditions, both common and corner test cases.
If you have issues with making the right test data available for your QA teams to validate the application under test (AUT) effectively due to shared backend databases, you need test data management.
Download Parasoft’s whitepaper for initiatives on how to reduce your test data management headaches.
Test Data Management Benefits
Types of Test Data Management
Test Data Management Best Practices
The process of procuring, owning, and securing test data is both a requirement and a liability. Without proper test data, you can’t achieve high test coverage, but you need to ensure the test data doesn’t contain any sensitive information that could introduce risk.
You need realistic data to test comprehensively every aspect of your code. But good data is difficult to access, difficult to secure, and difficult to store. Parasoft’s virtual test data solution solves these data stewardship headaches—and more.
Generate test data faster. Providing test data for a QA team is a critical need that is time-consuming; developers could better spend their time on code development. Automate test data generation and test data provisioning to eliminate delays and enable self-service access. This allows the DevOps team to build data centers and models, and for the testing teams to share and control them.
Satisfy the needs of multiple teams. When you use virtual data, you can provide appropriate, relevant, and purposeful datasets to each DevOps team so that everyone can make progress with testing throughout the software lifecycle. Make a copy to preserve the original values. The team can reuse the data and reset it to a known state, as needed.
Ensure repeatability for issue resolution. When the QA team does their testing, they may come across issues. By sharing their test dataset with the developers, they can reproduce the issue reliably in the dev environment so that developers and testers identify and address them.
Ensure efficient data governance and stewardship. Test data from hardware and real production environments are not always accessible. Virtual test data can mirror real-life data platforms and hierarchies while masking sensitive information to ensure compliance with regulations such as PCI DSS and GDPR.
Leverage modeling and subsetting. To ensure that your test data is suitable for your purpose, you can employ modeling and subsetting. The data source you use should be both accurate and valid but it also needs to cover corner cases and less common user paths. Some data should cause user failures to ensure that the process also validates error scenarios.
Protect sensitive data with masking. For applications that process sensitive information, such as medical records or financial information like credit cards, data masking protects against breaches and ensures regulatory compliance. However, this process frequently adds operational costs and extends test cycles.
Preserve data quality. Operation teams make a great effort to produce the correct kinds of test data, such as synthetic datasets or masked production data, to software development groups. When TDM groups weigh requirements for various kinds of test data, they need to also ensure the quality of the data. They must preserve the quality across three main areas:
Age of data. DevOps teams often cannot meet ticket requests because of the effort and time they require to formulate test data. Thus, data can become stale. This can negatively impact testing quality with the resulting pricey, late-stage problems. The TDM solution should focus on reducing the time required to refresh the environment, which makes the latest version of the test data more accessible.
Accuracy of data. When testers need a number of datasets at a specific time for systems integration tests, this can challenge the TDM process. For example, testing a pay procedure process may require that the process federates across inventory management, customer relationship management, and financial apps. The TDM process should permit the provisioning of multiple datasets to the same time point and concurrently reset between test sequences.
Size of data. Because of storage limitations, developers often must work with data subsets, which by nature may not satisfy every functional test requirement. Using subsets may result in missing case outliers, which ironically can increase infrastructure costs rather than decrease them because of errors related to enterprise data. The optimal testing strategy is for developers to provision full-sized test data copies, and then to share common test blocks across copies, thus using only a tiny fraction of subset space. The result is that TDM teams frequently reduce subsetting operating costs, both error resolution and data preparation costs, by reducing the need of data subsetting as often.
Learn how Alaska Airlines implemented Parasoft’s methods to test the untestable and work successfully in the real world.
Test Data Challenges
For applications driven by personalized customer experiences, the competition to win over customers is fierce. Test data managers orchestrating customer-facing and backend business operations require volumes of test data to ensure robustness.
Here are a few examples of situations your QA team may be facing.
“Help! Someone else changed/deleted my backend data!”
If multiple people share test datasets, there is a risk of someone modifying them and making them unusable by others on the team. Create duplicate datasets for individual users to avoid this issue.
“I must reload my backend datastore before every test run, causing testing wait times.”
When the test dataset is available in a virtualized test environment, the tester now has control over their own test data and no longer has to wait for reloading from the actual data store.
“My AUT is moving to a new test environment and my required backend datastores aren’t available.”
Isolate the AUT and the necessary test data in a virtual test environment to enable testing to continue uninterrupted until the new environment is fully up and running.
“The developers changed the database layout and now my test data doesn’t work.”
Use modeling to analyze if the production source has been modified, then update your test datasets to correspond to the latest configuration.
“Some link in the chain between my AUT and my backend datastore is broken.”
When the test environment is unstable, it can impact a tester’s daily activities, causing the data to become inaccessible. Virtualize the backend dependencies to keep them from being a bottleneck and enable the tester to create test data on demand.
“I want to edit my backend data, but I can’t because it will negatively affect other testers.”
Editing data in a shared datastore could corrupt the dataset for others, causing unexpected results that require debugging due to false-positive test failures. Allowing each tester to create and manage their own virtual test dataset avoids cross-data pollution.
Get Started With a Test Data Management Process
Gain independence and get more control over your day-to-day activities by putting test data into the hands of the testers with an effective, high-quality test data management process.
- Automatically create data models to generate and define test data and easily update it as often as needed.
- Extract data from any source, create synthetic data, and mask any sensitive data for use with virtual services.
- Store and manage the data in the integrated data repository for quick and easy access.
- Snapshot the data to roll forward and backward easily to set specific conditions and points in time.
Parasoft Virtual Test Data Tools
Parasoft’s extensive solutions create and manage virtual test data to plug and play into its automated testing solution so you can test continuously.
See the Value of Service Virtualization
Want to see how much service virtualization can benefit your testing processes?
Use this handy calculator to assess how Parasoft can help you decrease the time and costs of application testing by reducing constraints in the environment.
Just enter the number of people on your development and testing teams along with inputs for test environments, defects, and delivery delays. You’ll get a calculation that projects the value of the potential benefits you could experience by implementing the Parasoft service virtualization solution in your organization.
Frequently Asked Questions
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