feature-flag-test-matrix-reference

feature-flag-test-matrix-reference

Overview

A codebase with N feature flags, each having M variants, and users in K segments, has N × M × K possible flag-state-segment combinations. At realistic numbers (50 flags, 2 variants each, 5 segments) that's 500 - and at 50 flags with 3 variants and 10 segments, it's 1500. Testing every combination is infeasible.

Per launchdarkly.com/blog: "The matrix grows exponentially; pick coverage smartly."

This skill is a pure reference consumed by the SDK-test + coverage-builder skills.

When to use

• Designing the test surface for a new flag-heavy product.
• Auditing existing flag-test coverage - are critical combinations covered?
• PR review of a new flag - does it create a coverage gap?
• Investigating an "only happens with flag X + flag Y on" incident.

The combinatorics

Variable	Typical scale
Total flags in codebase	20-500
Variants per flag	2 (most), 3-5 (experiments), 10+ (multivariate)
User segments	5-20 (free, paid, enterprise, internal, beta, etc.)
Combinatorial total	Quickly enters thousands

Insight: most flag combinations are inert (independent). Only a small subset interact - the test matrix should target interactions.

Five coverage strategies

1. Default-only smoke

Test only the default-value combination ("all flags off" or "all flags at default"). Fast but misses everything.

Use when: flag-heavy codebase where defaults change rarely.

2. Per-flag isolation

For each flag, test default + each variant in isolation. N × M tests; ignores interactions.

Use when: flags are mostly independent (UI tweaks, language strings, low-risk).

3. Pairwise interaction

Test every pair of flags (combinatorial 2-way coverage). Per NIST SP 800-142 on combinatorial testing, pairwise catches ~67% of real defects with O(N²) combinations.

Use when: flags are known-interacting (auth + permissions, billing + plan-tier).

Implementation: tools like pict (Microsoft) generate the pairwise matrix from a flag inventory.

4. Full matrix

Every combination. N^M tests for boolean flags.

Use when: small (≤10) flag count with strong interaction; financial / regulatory paths.

5. Risk-driven

Custom matrix targeting (flag, segment) cells with known risk (per a risk register per qa-process/risk-matrix).

Use when: any non-trivial codebase. Best in practice.

Special flag-state test categories

Category	Test
Kill-switch	Setting flag → off must halt the feature within N seconds (cache TTL)
Percentage rollout	Flag at 10% → ~10% of users in 'on' bucket; SDK assignment stable per user
Targeted rollout	Targeting region=EU → only EU users get treatment
Sticky assignment	Same user → same variant across sessions and re-launches
Override hierarchy	User-specific override > segment override > default
Default-on-error	SDK fails / network down → default value returned
Fast-deactivate	Toggle flag off → live users see new state on next evaluation

These are per-platform behaviours (LaunchDarkly, Unleash, Flagsmith, GrowthBook implement them differently); test per platform per the SDK skills.

Flag-test layering

Tests should run at multiple layers:

Layer	Coverage
Unit	Resolver / handler logic gated on flag value (mock SDK)
Integration	SDK + handler together (test SDK against local-eval / fixture)
E2E	Real flag toggle → real user sees the change
Production smoke	After flag change → assert expected behaviour live

Flag-experiment distinction

Per qa-experimentation/ab-test-validity-checklist:

Flag	Experiment
Toggles behaviour	Measures outcome
Boolean / multi-variant	Multi-arm with metrics
Test the behaviour change	Test the assignment + outcome correlation
Ship decision on engineering judgment	Ship decision on statistical result

A feature flag can power an experiment (the flag is the allocation mechanism), but tests are layered: flag tests verify correct behaviour per variant; experiment tests verify assignment + analytics.

Anti-patterns

Anti-pattern	Why it fails	Fix
Test only default-value path	Misses every flag-on case	Per-flag isolation minimum
Mock the SDK to return constant	Misses targeting / rollout logic	Local-eval mode or fixture-based SDK
Same test for every flag combination	Slow; flaky; opaque failures	Per-combination assertion logs
No kill-switch test	Production incident has no rehearsed response	Test deactivation latency
Don't test percentage-rollout sticky-assignment	Rollout produces non-deterministic UX	Per qa-experimentation/ab-test-validity-checklist
Tests assume flag-on default	Real default-off behaviour untested in CI	Test both paths
No cleanup test for removed flags	Stale-flag accumulates per flag-removal-runbook-author	Periodic stale-flag audit
Pairwise without flag-interaction discovery	Some pairs spuriously interact	Couple with risk-register input

Limitations

• Pairwise misses 3-way+ interactions. Some real-world bugs need 3-way coverage.
• Real-world matrices have ordering effects. Flag A enabled THEN flag B may differ from B then A; test ordering needs separate coverage.
• Coverage tooling lags. PICT / ACTS exist but integration with flag platforms is bespoke.
• Stale flags pollute the matrix. Cleanup pairs with flag-removal-runbook-author.

References

• LaunchDarkly flag-testing strategy: launchdarkly.com/blog/.
• NIST SP 800-142 (combinatorial testing): csrc.nist.gov/publications/detail/sp/800-142/final.
• GrowthBook test docs: docs.growthbook.io/lib/node.
• Sibling catalogs: flag-state-coverage-builder, flag-removal-runbook-author.
• Consumed by: launchdarkly-testing, unleash-testing, flagsmith-testing, growthbook-testing, stale-flag-detector.
• Cross-plugin: qa-experimentation/ab-test-validity-checklist, qa-test-environment/feature-flag-test-harness.