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, so the matrix has to be sampled deliberately rather than enumerated.

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.

How to use

1. Inventory the flags: list every flag, its variant count (M), and the user segments (K) it targets.
2. Size the problem: compute N × M × K to confirm full enumeration is infeasible and sampling is required.
3. Discover interactions: mark flags as inert (independent) or known-interacting (auth + permissions, billing + plan-tier), pulling from a risk register where one exists.
4. Pick a coverage strategy from the five below - default-only smoke, per-flag isolation, pairwise, full matrix, or risk-driven - matching the flag set's risk.
5. Add the special flag-state categories (kill-switch, percentage-rollout, sticky-assignment, default-on-error) as their own tests.
6. Layer the resulting cases across unit, integration, E2E, and production-smoke.
7. Hand the matrix to the platform SDK skill (launchdarkly-testing, unleash-testing, flagsmith-testing, growthbook-testing) to implement each case.

The combinatorics

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 risk-matrix in the qa-process plugin).

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

Special flag-state test categories

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:

Flag-experiment distinction

Per ab-test-validity-checklist (in the qa-experimentation plugin):

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.

Worked example

A billing service has 3 boolean flags - new_checkout, annual_discount, tax_engine_v2 - across 3 segments (free, paid, enterprise). The team knows annual_discount and tax_engine_v2 interact: a discount changes the taxable amount.

1. Inventory: 3 flags, 2 variants each, 3 segments; the full flag-state-by-segment cross-product is too large to enumerate.
2. Interaction discovery marks the annual_discount × tax_engine_v2 pair as known-interacting; new_checkout is a UI change treated as independent.
3. Strategy: risk-driven. Per-flag isolation covers new_checkout; a pairwise cell covers (annual_discount on, tax_engine_v2 on) on the paid and enterprise segments where money moves.
4. Special categories: a kill-switch test asserts toggling tax_engine_v2 off reverts to v1 within the cache TTL; a sticky-assignment test asserts the annual_discount percentage rollout keeps each user in the same bucket across sessions.
5. Layering: unit tests exercise the tax resolver per flag value; an E2E test toggles tax_engine_v2 and asserts an enterprise invoice total.

Result: a handful of targeted cases instead of the full cross-product, with the discount × tax interaction covered explicitly.

Anti-patterns

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.
• Cross-plugin: ab-test-validity-checklist, feature-flag-test-harness.