Dynamic automated risk based test selection - test efforts where it matters most

A key factor of quality assurance at Westermo is dealing with complexity. Infinite possible combinations in terms of new and existing features, supported protocols, supported hardware platforms, network topologies and solutions is the challenge we meet.

At the same time we want to be flexible when developing new features and to get rapid feedback. We manage this with feature-driven development and continuous integration.

With our automated system level regression test capabilities, we have the ability to pinpoint problems long before product release with early feedback to developers. With this we are able to automatically provide diversified results tailored for each individual developer without the need to manually choose what to test and when.

We have our own developed test framework that completely automatically selects what tests to put into each individual test suite that the framework. This selection is based on quantified risks that changes along with what is about to be tested. The test framework also maps test cases towards an arbitrary number of physical test systems, each with its different set of DUTs. We have built the capability into the test framework to be able to test different network topologies that even comprise of subsets of our physical test network topologies. This helps us mitigate risks and manage the complexity that comes from an infinite number of possible test setup combinations.

At the CODE-STHLM Conference (Continuous Delivery and DevOps Conference Embedded Edition) I will talk about how we at Westermo continuously ensure high quality of our software development throughout the development process with our automated system level regression test capabilities.

Please come listen to my speech at the conference, to learn how this works for real and come see me afterwards to discuss it with me. I am looking forward to meet you there!

Read more about the conference

- Petra Wernkvist, Test Lead at Westermo

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