that is highly scalable and focused on testing.
Pass or fail? More
Failures are boring, now observe regressions and fixes, see behavior of test cases over time on charts. Look at changes, compare with previous results. Unstable tests are automatically identified and marked.
More dimensions to results
Performance testing, obviously. Now with support for multiple iterations, statistic analysis (avg, median, stdev, cv and more). And with automatic regression detection.
Common way of executing jobs is doing this directly in local system. Beside that Kraken allows for execution in containers (Docker or LXD) or in virtual machines (AWS EC2).
Efficient and standardized pre-commit testing
Reduced risk of regressions. Developers can test their code with the same tests that are used for product validation. No need for expensive individual bench test environments.
Broad spectrum of execution environments
Testing on a standard hardware is easy. What about automated testing on unstable pre-production hardware platforms? Or in an simulation environment, when real hardware is expensive or does not exist? And with a variety of automatically deployed operating systems.
Cloud & Autoscaling
Kraken can operate in the cloud. In can be easily deployed to the cloud but also can automatically spawn new executing machines there if the demand is high. Currently the autoscaling feature is supported in AWS.