A test automation platform that selects the right tests for each code change in real-time. Test more often, find issues earlier, and accelerate your entire dev cycle.
Simple CLI commands plug neatly into build scripts. Many teams are able to get started in under an hour without difficulty.
Many apps, languages, and frameworks work out of the box. Nothing about the Launchable algorithm is particular to a specific platform.
Launchable works with many different kinds of automated tests. From unit tests to integration, from system tests to UI, we have you covered.
Make your entire dev team more productive!
The Launchable AI uses machine learning to identify the best tests to run for a change allowing you to run fewer tests. This can drastically reduce pipeline run time up to 80% or more.
Each project in Launchable is associated with 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.
On each change, the Launchable AI 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 one will be found.
Tired of getting feedback from long-running test suites only after merging? Subset a portion of your test suite and move it earlier in your workflow with Launchable.
Shorten test feedback delay by running tests that are likely to fail, first.
Subset long-running suites and shift-left. Run a subset on every PR or at the top of each hour.
Move a subset of your long-running UI suite earlier in your development cycle.
Using canary deployment? Shift tests that are unlikely to fail post-deploy.
Your DevOps transformation doesn't need to take years!
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.
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.
Eliminate slow test cycles. Test what matters.
