What would disprove this analysis? (Criterion 1)

Organizations with 100+ feature flags experience no increase in production incidents from flag interactions

What would disprove this analysis? (Criterion 2)

Stale feature flags are consistently removed within 30 days of full rollout without dedicated cleanup effort

What would disprove this analysis? (Criterion 3)

Combinatorial code paths from feature flags are fully tested in CI without exponential test growth

When should you avoid feature flag debt?

You don't have a process for removing flags after rollout completes. Your team treats feature flags as permanent configuration rather than temporary deployment tools

What are alternatives?

Branch by abstraction: Use code abstractions instead of runtime flags — cleaner, testable, no flag debt. Short-lived feature branches: Small, frequent merges with trunk-based development — less need for flags. Canary deployments: Deploy to a subset of infrastructure rather than flagging in code — no code complexity

Catalog

T013

Technology

Feature Flag Debt

MEDIUM(78%)

February 2026

4 sources

Context

Feature flags enable safe rollouts — deploy code behind a flag, enable for 1% of users, monitor, then roll out fully. The practice is sound. The problem is what happens after. Flags that were meant to be temporary become permanent. Nobody removes them because nobody knows if they're still needed. The codebase accumulates hundreds of flags, creating a combinatorial explosion of code paths that are never tested together. The safety mechanism becomes a source of bugs.

Hypothesis

What people believe

“Feature flags enable safe, controlled rollouts without long-term costs.”

Actual Chain

→

Temporary flags become permanent — nobody removes them(Average codebase accumulates 50-200+ stale flags)

└

Removing a flag requires understanding its purpose — often undocumented

└

Fear of breaking something prevents cleanup — 'just leave it'

└

Flag ownership unclear — the person who added it left the company

→

Combinatorial explosion of code paths(N flags = 2^N possible states, most never tested)

└

100 flags = 10^30 possible combinations — impossible to test

└

Bugs appear only in specific flag combinations that nobody anticipated

└

Debugging requires knowing which flags were active for the affected user

→

Code complexity increases with every flag(if/else branches multiply throughout the codebase)

└

Reading code requires understanding which flags are active in which environments

└

Refactoring becomes dangerous — flag interactions are unpredictable

└

New developers can't understand the codebase without a flag glossary

→

Flag management becomes its own infrastructure burden(Flag service becomes a critical dependency)

└

Flag service outage = unknown application behavior

└

Flag evaluation adds latency to every request

Impact

Metric	Before	After	Delta
Stale feature flags in codebase	0	50-200+	Accumulating
Untested code path combinations	Manageable	Exponential (2^N)	Untestable
Time to understand code with flags	Baseline	+30-50%	+40%
Bugs from flag interactions	Zero	5-10% of production incidents	New bug category

Navigation

Don't If

•You don't have a process for removing flags after rollout completes
•Your team treats feature flags as permanent configuration rather than temporary deployment tools

If You Must

1.Set expiration dates on every flag — auto-alert when a flag is older than 30 days
2.Assign an owner to every flag — ownership transfers when people leave
3.Run regular flag cleanup sprints — treat stale flags like technical debt
4.Limit total active flags — set a hard cap and enforce it

Alternatives

Branch by abstraction — Use code abstractions instead of runtime flags — cleaner, testable, no flag debt
Short-lived feature branches — Small, frequent merges with trunk-based development — less need for flags
Canary deployments — Deploy to a subset of infrastructure rather than flagging in code — no code complexity

Falsifiability

This analysis is wrong if:

Organizations with 100+ feature flags experience no increase in production incidents from flag interactions
Stale feature flags are consistently removed within 30 days of full rollout without dedicated cleanup effort
Combinatorial code paths from feature flags are fully tested in CI without exponential test growth

Sources

1.
Martin Fowler: Feature Toggles
Canonical reference on feature flag categories and the importance of managing flag lifecycle
2.
LaunchDarkly: Feature Flag Best Practices
Industry guidance on flag management including lifecycle, ownership, and cleanup
3.
Google: Testing with Feature Flags at Scale
Google's experience managing thousands of flags and the combinatorial testing challenge
4.
Knight Capital: $440M Loss from Flag Misconfiguration
A stale feature flag contributed to Knight Capital's $440M loss in 45 minutes

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