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●2.1.301 — Adds the vim % motion, LaTeX math rendering in the VS Code panel, and a retry-watchdog limit for usage-limit waits (10/15)●2.1.299 — Conversations over 200K tokens left idle about 54 minutes get compacted; MCP allow rules now match only the server they name●SONNET 4.5 — Retires on the Claude API on 11/30, 45 days left. Migrate to Sonnet 5.5●WINDOWS — Reports say hooks break silently only on Japanese-locale Windows; check the character encoding first●FAST MODE — Opus 5.5 fast mode is out of research preview and no longer needs the beta header (10/15)●NEW — I Put a Ten-Line Promise File at the To…●2.1.301 — Adds the vim % motion, LaTeX math rendering in the VS Code panel, and a retry-watchdog limit for usage-limit waits (10/15)●2.1.299 — Conversations over 200K tokens left idle about 54 minutes get compacted; MCP allow rules now match only the server they name●SONNET 4.5 — Retires on the Claude API on 11/30, 45 days left. Migrate to Sonnet 5.5●WINDOWS — Reports say hooks break silently only on Japanese-locale Windows; check the character encoding first●FAST MODE — Opus 5.5 fast mode is out of research preview and no longer needs the beta header (10/15)●NEW — I Put a Ten-Line Promise File at the To…
Articles/Claude Code
⟐ Claude Code/2026-07-03Advanced

When a Claude Code Refactor Passes Every Test but Behaves Differently in Production — Catching Silent Contract Drift with a Behavior Diff Harness

Hand Claude Code a large refactor and your tests can stay green while production behavior quietly shifts. Here is how I record exception channels, log shape, init order, and return values as a signature, then diff them per commit to catch contract drift before it ships.

Claude Code264Refactoring3Contract TestingObservability5Regression Detection

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You ask Claude Code to reshape a module into a new architecture, you review the diff, the tests are green, you ship with confidence — and a few days later someone on the operations side quietly asks, "did the behavior change?" That nagging feeling, the one that slips through the net of unit tests, is the scariest part of a large refactor for me.

As an indie developer, I run personal apps and the automated publishing backends for several technical blogs, and I stepped straight into this while rewriting one of those pipelines end to end. Every test passed, yet one branch of the publish path silently no-opped, and I didn't notice for days. The cause: surrounding code depended on a "swallow the exception and return a default" contract, and the refactor had made it throw honestly instead. No test verified that contract, so it stayed green while broken.

This article is the tooling I now use to catch "works but broken" in a layer separate from tests. At the center is the idea of a contract snapshot — recording the observable behavior of code as a signature and diffing it before and after — plus a harness you run per commit.

Why a big diff hides "works but broken"

Claude Code is capable, so most refactors come back as working code. The problem is that the larger the diff, the wider the gap grows between "it runs" and "it runs under the same contract as before."

By contract I don't only mean explicit things like type signatures. The nastier ones are implicit contracts — assumptions that function as prerequisites without being written anywhere.

Implicit contractWhat breaks when it changes
Throws vs. returns a defaultThe caller's swallow-and-continue assumption collapses; a batch halts midway
Log line structure (key set, ordering)A monitoring regex silently stops matching and alerts go quiet
Order of init / connection setupLoad-dependent failures, e.g. connection exhaustion only while idle
null vs. empty, rounding directionAggregates shift slightly and a downstream threshold flips

Unit tests are good at checking equality of return values, but they rarely cover these behavior signatures. So we slot in a layer, separate from tests, that records the signature itself before and after the refactor and compares them. That is the contract snapshot.

Contract snapshots — what to sign

A contract snapshot hits the target code path with a set of representative scenarios (probes) and folds the observable behavior of each run into a structured record. The key is to include not just the return value but which channel the result came back through.

For each probe, I record at least this:

  • The result value (normalized so it is comparable)
  • The channel the result returned on (a normal return, or an exception — and if an exception, the type name)
  • The "shape" of the log lines emitted during the probe (not values, but the set of keys and the ordering of levels)
  • The order of side effects (a labeled sequence of DB connects, external calls, and so on)

Recording shape rather than value is the point. Timestamps and IDs in log bodies change every run, so comparing them directly floods you with noise. By reducing to the skeleton — key set and level ordering — you only raise a diff when the structure your monitoring depends on actually changes.

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WHAT YOU'LL LEARN
✦A contract-snapshot design that folds exception channel, log shape, init order, and normalized return value into one signature and diffs it before and after a refactor
✦A complete, drop-in Python harness that detects drift per commit and blocks it via a pre-push hook and a CI gate
✦A decision rule for when drift appears: whether to revert the change or deliberately update the contract, judged by the shape of the series and the presence of intent
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