Experiment Coverage
Experiment coverage is the share of product changes that are instrumented, tested, or released through an experimentation-first flow rather than launched blind.
Key points
- Statsig treats coverage as a core enterprise experimentation lever: when only a small fraction of features are tested, experiments remain optional and are easy to drop under timeline pressure [src-036].
- At high coverage, experimentation becomes a release gate: features do not truly ship until data says they are safe or valuable [src-036].
- Enterprises struggle with coverage because parallel roadmaps, legacy code paths, and quarterly pressure all encourage “just launch it” behavior [src-036].
- Partial coverage creates compounding blind spots: teams over-index on the few things they measure, and leadership may believe incomplete trend lines [src-036].
- The article recommends integrating feature flags and experiments so every feature can be a test by default [src-036].
- It also recommends aligning engineering KPIs with metric impact rather than feature launch and sunsetting legacy code that cannot be instrumented [src-036].
Related entities
Related concepts
- Enterprise-Scale Experimentation
- Feature-Gated AI Code Rollouts
- Parallel A/B Testing
- A/B Test Acceleration
- Outcome-Obsessed Product Management
Source references
- [src-036] Yuzheng Sun — “Addressing complexity in enterprise-scale experimentation” (2025-04-23)