Launchable

Launchable uses machine learning to analyze the relationship between code changes and test failures, predicting which tests are most likely to catch bugs for any given commit. It builds a statistical model from historical test results and source code changes, learning which tests tend to fail together and which tests are relevant to specific areas of the codebase. By intelligently prioritizing and selecting a subset of tests, it can reduce CI feedback time by up to 80% without sacrificing confidence in release quality.

The platform is framework-agnostic and integrates into existing CI pipelines with minimal configuration -- typically a few lines added to the CI script. It supports pytest, JUnit, RSpec, Go test, Bazel, and many other test runners through a lightweight CLI. Enterprise teams benefit from centralized dashboards showing test suite health, prediction accuracy, and time savings across projects, with API access for custom integrations and reporting.

Launchable is designed for engineering organizations with large, slow test suites where running the full suite on every commit is impractical. Its differentiator is the predictive test selection approach: rather than optimizing how tests run (parallelization, caching), it optimizes which tests run. This complements existing test infrastructure rather than replacing it, making it straightforward to adopt incrementally without re-architecting CI pipelines or migrating test frameworks.