Software testing tools to support a DevOps approach

Latest Articles

Secure DevOps | October 29, 2020
Accelerating Testing at Reduced Costs
For many organizations, defining test processes to keep up with agile and continuous software delivery trends is a huge challenge.
Secure DevOps | October 29, 2020
Data-driven Test Acceleration Made Easy
One of the major challenges with distribution testing is data-driven tests. All tests that are running simultaneously should be able to access the data.
Secure DevOps | October 26, 2020
Enhancing Test Results Via Unified Reports
Evaluating software components under unexpected or expected conditions is very crucial. Therefore, the testing team plays a significant role in business growth by evaluating the quality of the product.
Secure DevOps | October 16, 2020
Achieving Industry Standard Certification for Embedded Software Testing
Automating the creation and deployment of component test harnesses, test stubs and test drivers is a cinch thanks to HCL OneTest Embedded. With a single click from any development environment, testers can profile memory and performance, analyze code coverage and visualize program execution behavior.
Secure DevOps | October 8, 2020
HCL OneTest – Moving Towards Intelligent Data Generation
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.  Historical Approach 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...
Secure DevOps | August 28, 2020
Virtualizing Middle-Tier and Back-end Applications
During the testing process, delivery is often challenged by the fact that different development teams move at different velocities.

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