testing-regression-strategy

A risk-based regression strategy selects tests by evaluating execution cost, flakiness, and detection value against a change-impact model to maximize risk reduction within your CI budget. It outputs a tiered policy balancing coverage and time constraints.

A tiered regression policy categorizes tests into always-run, conditional, and periodic execution tiers based on risk and CI budget constraints. You need one when not all regression tests can run on every change and a structured selection policy is required.

Build a regression policy by supplying a release risk profile, CI budget, test inventory with cost and flakiness metrics, and a change-impact model. The strategy processes these quantitative inputs to generate a tiered policy, decision record, and gap register.

Risk-based regression coverage requires a change-impact and component criticality model alongside a test inventory and release risk profile to function. These quantitative inputs drive the test selection decisions and tiered policy generation.

Manage flaky tests by including flakiness as a quantitative metric in your test inventory alongside execution cost and detection value. The regression strategy uses this data to select tests that maximize risk reduction within your CI budget while minimizing unreliable executions.

Your regression suite exceeds the CI budget because it lacks a risk-based selection policy that filters tests by execution cost and change-impact. Applying a tiered regression policy ensures only the necessary tests run per change to stay within budget.