Red Hat announced new capabilities and enhancements across its portfolio of open hybrid cloud solutions aimed at accelerating enterprise adoption of edge compute architectures through the Red Hat Edge initiative.
Start with Part 1: Beginning Your Test Automation Journey
Start with Part 2: Building Confidence in Automation
As we've stated throughout this series, the end goal of test automation is to enable frictionless, continuous testing in a high-throughput deployment pipeline. As you progress through the beginner and intermediate stages of test automation, you should notice a gradual increase in efficiency and release velocity. However, following a templated approach to test automation will only take you so far.
The expert stage of test automation is all about continuous optimization. More specifically, this phase is about collecting data about your existing process, analyzing that data to derive quality insights, applying those insights to improve your practice and then measuring these improvements as part of repeating the cycle again. There are three key steps to realize continuous optimization.
Step 1: Do Just Enough Testing at Each Phase of Deployment
To prepare yourself for successful continuous optimization, you should first take a step back to ensure you are doing the right amount of testing at each stage of your deployment process. How much unit and initial integration testing are you doing? How many smoke and sanity tests are running sooner rather than later to ensure which builds are stable, and which warrant additional downstream testing? When are you running your regression tests and your later-stage manual tests? Where does your non-functional or other costly testing fit in your pipeline?
It is important to analyze your pipeline and verify you are doing just enough testing at each stage because it allows you to halt if you have an issue at a particular stage. This testing approach is really the first step of continuous optimization because it is both cost effective and establishes multiple, measurable milestones in your testing pipeline to measure. If you spread the process out and test incrementally, you can start collecting data at every single stage. Make sure to have quantifiable quality gates at each of these stages. This helps drive which measurements to take during the testing process.
Step 2: Collecting Metadata About Your Testing Process
At each phase of testing, think about what data you can collect and feed into a repository so you can mine it later. Focus on at least these key questions while you are implementing your metadata collection strategy:
■ What stage of the testing process are we looking at?
■ What build or milestone is under test?
■ How many tests were run?
■ How long did each test take?
■ What platforms were tested on?
■ Which tests passed and which ones failed?
■ Is the ratio of passed-to-failed tests acceptable for that particular quality gate?
■ How long is it taking to triage automated test failures?
■ Was the build kicked back or is the deployment process continuing?
■ What bugs were associated with this build?
Collecting test metadata that answers questions like these at each phase allows teams to compile substantial insights in the future. Especially when munging this data with data from other teams (e.g. engineering, marketing, etc.).
Step 3: Making Data-Driven Decisions
Now that you have captured data about your testing process, you can organize and visualize it using tools like Splunk or Domo to make it more digestible.
Once you have your data in a dashboard, it’s time to actually do something with it. You may, for example, look at your data and determine that a subset of your automated tests are not providing the right value for your team. This is a common situation where a few very complex tests have been automated but don’t run reliably. By collecting the data mentioned above, you should be able to measure the impact such unreliable tests are having on your release process. You may instead try adding those tests to the manual suite and then measuring how that improves your test times.
To take things a step further, you can also incorporate data from other departments into your insights to further refine your testing strategy. For example, consider munging development code coverage data into your quality decisions. This can help you literally visualize what your testing triangle looks like. Also consider pulling marketing insights into your datasets to cross reference real-time customer usage data with your testing strategy. Your customer’s usage patterns will change and evolve over time as your application grows and new features are introduced. It’s important to stay on top of how those usage patterns change so that you can quickly and continuously adjust your testing strategy appropriately.
Remember that improving your automation practice is an unending process. You are never truly done. There is always more that can be done. Following these steps while incorporating the lessons you’ve learned along the way will help you continually optimize your automation practice.