Shift-left testing, risk based testing, early testing and technical debt reduction are all predicted to be some of the most sought-after trends in product development life cycle. All of these can be achieved with continuous testing through DevOps, as it emphasizes quicker transitioning of quality products to production environment at a reduced business risk.
Software Engineer in Test (SEiT) or Software Developer in Test (SDiT) was long-spoken earlier and now implemented as part of DevOps culture. As the software side of the organization moves to continuous testing practices, so, too must the product side of the organization. External market forces including competition, innovation quotient, faster time to market, scalability and lean process initiatives definitely play a role in driving the continuous testing trend.
From a technical standpoint, test environment creation, full test coverage, regression testing and integration testing of multiple endpoints are key parameters to ensure a faultless production environment when a new feature is added.
Adopting Testing Tools for DevOps Implementation
Continuous testing implementation not only includes adoption of right tools and test automation, but also a cultural change to incorporate testing at each stage of the agile development life cycle. It will involve changes right from test planning, test scenarios creation, automation feasibility analysis to test execution.
Teams need to be restructured in an agile setup to align testers closer to developers and operations teams, and have common goals for product/software delivery. This involves changing the culture and organization structure, which generally takes a longer gestation time. Test management tools such as JIRA and TestNG can help expedite this structuring, as both parties would be aware of the product features that need to be delivered and can view the progress at all times. There also are various open-source tools, including Selenium, Appium, Python robot framework/pytest and JMeter, that aptly meet the needs of agile testing.
Regression testing generally takes up the bulk of the time, as new features need to be tested every time they are added to avoid critical failures. Hence, automating regression tests have become an imperative as part of continuous testing. JUnit, which is a unit testing framework, promotes test-driven development and early code quality improvement. Bamboo can run tests in parallel batches and support hundreds of build agents providing an early feedback to developers. Automation scripts for test data cleanup, load and execution saves the testing effort of the operations team. Selenium test automation framework for web applications is one of the most famous tools for continuous testing, as it works with a host of programming languages including Java, C#, Groovy, Perl, PHP, Python and Ruby. Appium is another tool used frequently for cross platform mobile application testing.
Ensuring an Effective Product Release
Testing UI components and functionality of a product through API-based test automation is another important phase in continuous testing. API test scenario creation also generally needs involvement from the ops team. Considering virtualization of devices, along with applications or services, will accelerate test setup environments. Device virtualization using Raspberry pi helps in testing end–to-end use cases in IoT scenarios. Network virtualization for performance testing ultimately helps improve uptime and reduce outages.
The orientation of continuous testing with continuous integration and continuous delivery pipeline within an enterprise will determine the long-term success and standing of the final product in the market, from a quality perspective.
About the Author / Urvashi Babaria
Urvashi Babaria is a Product manager at eInfochips working in new age areas like IoT, DevOps and CloudOps. She is a techno commercial marketer with nine years of experience in product and project management, business analysis and transformation. She has keen interest in areas of data science including visualization, analytics and modeling. Connect with her on LinkedIn.
