In today’s world, testers face challenges to achieve the desired level of testing when the complexity of the data continues to increase day by day and the delivery time is reduced. With the kind of huge data volume and variety to manage, testers face challenges to create test data that covers all possible test scenarios. Most of the time, the major roadblock to achieving the desired result of testing is the lack of test data. Test data is a critical part of any application testing. To achieve success towards quality testing in a short duration of time, generating synthetic data becomes essential.
The traditional approach was to create a copy of the production data and then to use it as test data after masking and evaluating the relevancy according to the application. This test data is mostly accompanied by a Test Data Management (TDM) system to prepare, control, and use the data. All these overheads and manual approach results in delays.
The following figure shows the historical approach of creating test data from the production data. The production data is collected, cleaned, maintained, and then masked to hide the sensitive data. Each process is manual and has its own entry and exit criteria, which makes it time–consuming.
New Age Approach
The new approach attracts testers towards using the synthetic test data. The synthetic test data does not use any actual data from the production database, but it is generated artificially, based on the data model of the selected application. The best feature of these Test Data Generation (TDG) tools is to generate the synthetic test data on-demand. It generates the test data according to the test data scenario that meets the needs of a test case. The approach of the test data generation tool eliminates the need for the traditional TDM functions, such as data masking and obfuscation to meet the regulatory compliance guidelines. This approach results in saving time in the testing phase.
The following figure shows that instead of utilizing the production data, you can use the application schema to generate the test data without performing any step for data masking and cleaning. Being automated, this approach results in saving both time and resources.
The application testers have limited control over the quality of data that comes from the production. The testers are often required to manually modify the production data into the usable values to perform the application testing. Whereas, the synthetic test data reduces the effort of analyzing the data subset of the complex production and creating the test data. The test data is generated based on the schema modeling of your application. The schema model specifies the data definitions and combinations to cover all possible test case scenarios. This approach of generating the synthetic test data also reduces the consumption of time for complex schemas. In today’s market, though multiple solutions are available, HCL Software brings a one-stop solution to generate the test data and support for Continuous Integration (CI) and Continuous Delivery (CD) process. For CI/CD process, automated testing has become an essential technology.
HCL OneTest Data
HCL OneTest Data is a containerized, simple and powerful data fabrication tool. It is an automated and customizable tool to generate the synthetic test data for your testing environment with maximum coverage. HCL OneTest Data is one of the components of HCL One Test Server and can be accessed as Data Fabrication in the HCL OneTest Server GUI. It can generate large volumes of real-time test data and interoperates with various tools in a CI/CD process by using REST APIs. It also provides support for Kubernetes and Red Hat OpenShift application development and deployment platform.
The following figure shows the workflow of HCL OneTest Data:
Self-Service and Automation support – HCL OneTest Data is a fully GUI driven solution that does not require any technical expertise. It is easy to learn, use, and generate test data automatically by using its internal algorithms. Also, it provides a powerful built-in API to match the data with the generated data type.
External database support – HCL OneTest Data provides support to interact with external databases such as JDBC, SAP applications, MongoDB, and Excel files. By using this functionality, you can generate test data by using the data model of any of the external databases.
Reducing testing execution cycle – The test data is the core component of any application testing and creating test data manually is time-consuming. With HCL OneTest Data, you can generate huge volumes of intelligent data in short duration to perform application testing.
No Data Risk or Security issue – The extraction of real and potentially sensitive information from the production system can result in a data breach, but HCL OneTest generates synthetic data based on the internal algorithms that are applied to the schemas. By using this approach, you can test any application without the risk of data leaks or privacy issues.
Flexible and maximum performance – HCL OneTest supports the flexibility to model the data according to the testing needs. This feature helps in improving the efficiency and accuracy of pre-deﬁned or real-time data generation. It has inbuilt support for both single and multi-tenancy.
HCL OneTest Data also provides you the capability to perform data modeling for the test data generation by using the REST APIs. The following figure shows the sample screens of HCL OneTest Data:
The test data generation is one aspect of software testing. Creating the test data is a time-consuming task of the testing phase and hence involves one of the major cost involvements of the total software development. You can overcome the roadblocks on the testing path by using the test data generation tools.