Make CI pipelines faster by using machine learning to select tests
Launchable uses machine learning to identify the best tests to run for a change. This can drastically reduce pipeline run time up to 80% or more.
Each Launchable workspace has its own machine learning model. The model is trained by watching incoming changes and the resulting test failures. Typical training time for a model is 3-4 weeks,but it depends on your codebase and the frequency of test runs.
Run the right tests for a code change and nothing more
On each change, Launchable ranks tests by importance to code changes and allows you to create a unique subset (based on this ranking) in real-time. Run a fraction of your test suite while still maintaining high confidence that if a failure exists it will be found.
This graph a customer that is able to run 20% of the tests with Launchable to get 90% confidence that a failure will be found if one exists for a change. The practical outcome is that quality increases because you can test more often.
In addition to getting 100% confidence once a day, you can get 90% confidence every hour
Remove bottlenecks & double or triple hardware capacity
Hardware utilization drops when you run a smaller percentage of tests, effectively doubling or tripling resources. This allows you to reduce queue times and handle the same load with a third of the machines, or run 3 times the current load with the same hardware.
Mobile software: run tests with fewer mobile devices (Android and iOS)
Server-based software: run on fewer VMs and machines