What belongs here

Every entry compares two things: the version before a change and the version after, or the population exposed to it and the population that was not. The unit of observation is a change, not a system.

Contested placements

A/B Testing measures user outcome rather than correctness, and Incremental Build Correctness is arguably structural. Both are here because their verdict is about the effect of one specific change.

Where its signals arrive

  • Canary / shadow — Deploy stage. Does the new version behave differently from the old?
  • User outcome — Outcome stage. Does the system produce the intended result for users?

The atlas places every family on the same lifecycle grid; the framework defines the six dimensions each entry below is characterized along.

Entries (7)

Change Predictive

A/B Testing

Did users actually behave differently? An A/B test splits traffic between two versions and measures whether the treatment changes a user outcome metric — click-through, conversion, retention…

Change Predictive

API Compatibility

Did externally observable contracts change? A sensor of boundary stability — can old and new versions coexist?

Change Retrospective

Canary Analysis

Does the new version behave differently from the old version? A sensor of behavioral drift between deployments, measured on real traffic.

Change Retrospective

Error-Budget Impact

Did this change consume an abnormal amount of reliability budget? A sensor that directly ties code changes to user-visible impact.

Change Retrospective

Feature Flag Exposure Telemetry

A change behind a flag is not a change anyone has experienced. Flag evaluation streams answer “who is actually seeing the new behavior, right now?” — the difference between deployed and live, and the…

Change Retrospective

Incremental Build Correctness

Did the build system actually rebuild everything this change touched? Incremental builds and remote caches save hours, and silently shipping a stale artifact is the price when the dependency graph…

Change Retrospective

Shadow Traffic

Run the new implementation against real inputs without affecting users. A sensor that produces differential evidence with zero user risk.

Adjacent families