Launchable: Predictive Test Selection for Faster CI

Launchable applies ML to historical test outcomes & code changes to rank tests by failure probability, shrinking suites while preserving defect detection.

Core Features

• Dynamic Test Subsetting: Only run high-probability failure tests.
• Flakiness Insights: Identify unstable tests impacting velocity.
• Change-to-Test Mapping: Learns code areas ↔ test correlations.
• Continuous Model Improvement: Feedback loop each pipeline run.

Use Cases

• Large monolith or microservice repos with thousands of tests.
• Accelerating PR validation & developer feedback loops.
• Containing CI infrastructure costs.

Integrations

• Works with Jenkins, GitHub Actions, CircleCI, Buildkite.
• Supports JUnit, pytest, and other common frameworks.

Adoption Indicators

• Case studies citing >40% CI time reduction while maintaining detection.
• Used by engineering teams at notable large tech companies.

Best For

• Scale-stage engineering orgs constrained by test suite duration.
• Quality teams balancing speed vs. coverage.

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