Where can a QAOps framework be used?
The framework promotes increased collaboration between each engineering practice in the SLDC. This helps remedy the communication issues that often occur between testing QA teams in more traditional software development models, where QA is the binary pass-fail process that occurs just before release.
That’s the big-picture QAOps definition. Now, let’s investigate how QAOps can be used to improve specific testing processes.
1. Automated testing
Automation testing is a software testing technique that compares actual outcomes with expected outcomes. Automated testing automates the more mundane aspects of testing, allowing QA analysts to measure test results and advise subsequent stages of development based on the data these tests produce.
As it accelerates the quality feedback loop, automated testing is essential to a QAOps environment. However, before building an automation framework, QA specialists must study the product in detail to better understand its goals, specifications, and functionality.
Once this analysis has been performed, QA teams can decide which tests can be automated first, depending on the stage of the product their working on. Automated tests are then tailored to the goals of the product, saving time and making testing data more relevant.
2. Parallel testing
Parallel testing is the process of running multiple test cases on an application and its subcomponents across operating systems and browsers at the same time. These tests are automated and can drastically reduce total testing time, making parallel testing ideal fit to continuous integration, continuous delivery, and QAOps.
Parallel testing works well In QAOps, as it allows for accelerated testing within the delivery pipeline. However, given the amount of data that parallel tests process and produce, the demands on hardware and infrastructure will be greater. It’s vital to utilize a robust testing cloud that can handle the increased data processing load required of multiple tests in tandem.
In cases where server capacity allows, teams can launch CI/CD pipelines with automated and smoke tests in multiple parallel streams. This helps to quickly identify “flaky” tests—tests that exhibit both a passing and a failing result with the same code. Detecting flaky tests earlier in the SDLC helps teams find and eliminate unstable tests in their application.
3. Test scalability
After a product launch, it’s time to think about scalability. Product managers and product designers consider customer feedback when thinking about what features to add or improve next.
But when scaling a product, it’s also important to consider how tests will scale with the product. Scalability testing is a non-functional test methodology that measures the performance of a system when the number of user requests is scaled up or down.
Scalability testing helps to define a system or application’s performance at different conditions by changing the testing loads. The results of these tests show how the system or app will respond at scale. This data is important because, in a CI/CD model, tests must synchronize to scale up and down with the pipeline.
Automated tests are easier to scale than manual. With automated tests, engineers can save steps, models, methods, page objects, and features, and reuse them in future tests. Since the components have already been built, process automation makes the development of new tests less complex and easier to build with each step.
As a common QAOps practice, quality assurance teams must have the scalable infrastructure for performing testing to be able to increase the speed of tests, when required.
4. Smoke testing
Smoke testing, also known as “build verification testing” or “confidence testing,” determines whether a deployed build is stable or not. If features don’t work or bugs haven’t been fixed, testing is halted to prevent developers from wasting time installing a broken test build.
