Introduction

Most teams treat regression testing as something that happens after code is written and pushed: you open a pull request, CI runs, and then you find out what you broke.

The Analog Regression Lab flips that default.

Instead of relying solely on automated suites and post-hoc debugging, the Lab emphasizes paper-first routines—structured thinking, writing, and cross-checking before you run tests. The core idea: if you can articulate what should (and shouldn’t) happen in plain language, you can catch many regressions before your test suite ever spins up.

In this post, we’ll walk through the main components of the Analog Regression Lab:

• Paper-first routines for capturing expectations and edge cases
• Testora, a technique for using natural language as a regression oracle
• Treating docs, requirements, and commit messages as first-class test oracles
• Using a regression test case matrix to focus on the riskiest areas
• Structured checklists to standardize reviews and reduce oversight
• How this approach complements automated tests and surfaces design flaws earlier

Why Paper-First? The Case for Analog Regression

Most regressions slip through for a simple reason: the team never concretely wrote down what “correct behavior” meant for a given change.

A paper-first regression practice forces engineers to:

• Clarify expectations before coding or running tests
• Enumerate edge cases and failure modes on paper
• Expose ambiguities in requirements and design assumptions

This has two big effects:

1. You catch bugs just by thinking and writing more rigorously.
2. When tests do fail, you have a written explanation of what you believed should happen and why.

In other words, the Analog Regression Lab isn’t anti-automation; it’s anti-guesswork.

The Core Routine: Paper-First Before You Hit “Run”

The Lab’s baseline routine can be summarized in one question:

“What would you write down about this change if you were not allowed to run tests yet?”

Before running a single command, engineers create a lightweight Analog Regression Sheet for the change. It usually contains:

1. Change summary

   • What did you intend to change?
   • What must not change?

2. Expected behaviors

   • For each impacted feature, describe the expected input–output behavior in natural language.
   • Include both “happy path” and error conditions.

3. Edge cases and constraints

   • Boundary conditions (e.g., limits, empty states, race conditions)
   • Performance or scaling assumptions

4. Regression-sensitive areas

   • Which modules, APIs, or user flows are historically fragile?
   • What past incidents could recur?

5. Observability plan

   • Which logs, metrics, or dashboards will you check to confirm behavior in staging or production?

This sounds like “just more documentation,” but it’s not. It’s a temporary, targeted regression plan attached to a specific change. Most of the time, it takes 10–20 minutes to draft—and the payoff is catching regressions before you waste hours on broken CI runs.

Testora: Turning Natural Language Into a Regression Detector

The Analog Regression Lab introduces Testora, a technique that treats natural language descriptions of system behavior as regression oracles.

Instead of only relying on assertions in code, Testora asks:

“Can we compare how the system behaves now vs. what our written descriptions say should happen—and flag mismatches automatically?”

How Testora Works Conceptually

1. Collect natural language artifacts

   • Requirements documents
   • Design docs
   • Commit messages and PR descriptions
   • Incident reports or postmortems

2. Extract behavioral claims
   For example:

   • “If a user enters an invalid email, the system must not create an account and must show an error message.”
   • “Requests to /v1/payments must be idempotent for duplicate request IDs.”

3. Link claims to code and tests

   • Trace each claim to modules, endpoints, or functions that implement it.
   • Connect to existing tests where possible.

4. Compare current behavior vs. stated behavior

   • Record current system responses or behaviors (via tests, scripts, or manual runs).
   • Check whether those behaviors still satisfy the natural language claims.

5. Flag regressions and ambiguities

   • If behavior diverges from description → potential regression.
   • If description is ambiguous or conflicting → requirements/design issue.

The point is not magic NLP; it’s the discipline of using written intent as a test oracle. Testora formalizes a habit: whenever behavior changes, you reconcile reality with what your artifacts say should be true.

Natural Language as a First-Class Test Oracle

Traditional testing treats code and test files as the only “real” sources of truth, while requirements and docs are “soft” references.

The Analog Regression Lab inverts that priority:

• Requirements, specs, and design docs are first-class oracles.
• Commit messages and PR descriptions are micro-oracles stating what changed and what should remain stable.
• Incident reports are negative oracles, describing behaviors that must never recur.

By treating these artifacts as oracles, you:

• Make regressions visible whenever behavior drifts from documented intent.
• Encourage engineers to write clearer, more testable descriptions of behavior.
• Build a living bridge between “what we meant” and “what the code does now.”

This approach doesn’t replace assertions in code; it anchors them. A failing test is more interpretable when you can tie it back to a specific sentence in a requirement or commit.

The Regression Test Case Matrix: Focusing on What’s Risky

Not all regression tests are equal. Some guard critical user flows; others protect obscure corners.

The regression test case matrix is a simple but powerful tool for making this distinction explicit. The matrix usually includes columns like:

• Area / feature
• Change impact (None / Low / Medium / High)
• Risk level (Low / Medium / High / Critical)
• Frequency of change (Rare / Occasional / Frequent)
• Historical issues (Yes/No; link to incidents)
• Test coverage type (Unit / Integration / E2E / Manual)
• Regression priority (P0–P3)

Before or during implementation, engineers fill in the matrix for the change at hand. The result:

• High-risk, frequently changing areas get P0/P1 regression tests.
• Low-risk, stable areas may get lighter checks or rely on existing suites.
• You avoid the trap of treating every test as equally important.

Over time, this matrix becomes a navigational map of your system’s regression surface—where you’re most likely to get hurt if something drifts.

Structured Checklists: Reducing Variation Across Teams

Even experienced engineers miss things—especially under deadline pressure. The Lab leans heavily on structured checklists to make good regression habits repeatable.

A typical Analog Regression Checklist might include:

• Have you written a brief change summary and non-goals?
• Have you listed expected behaviors and edge cases in plain language?
• Have you updated or created natural language oracles (requirements, docs, or PR description)?
• Have you identified high-risk areas using the regression test case matrix?
• Have you mapped which tests (existing or new) cover those areas?
• Have you considered observability (logs, metrics, alerts) for this change?
• Have you flagged any ambiguous or conflicting requirements for clarification?

These checklists:

• Standardize expectations across teams and reviewers.
• Make regression thinking visible in code reviews.
• Provide a tangible artifact that can be audited or improved over time.

Instead of relying on “experience” or “intuition,” you embed regression discipline into the workflow.

Complementing (Not Replacing) Automated Test Suites

The Analog Regression Lab is explicit: this is not a substitute for automated testing. It’s a force multiplier.

By shifting effort to pre-test reasoning and documentation, you:

• Catch obvious misalignments before CI ever runs.
• Write better, more targeted tests because you’ve already articulated behaviors and edge cases.
• Make failing tests easier to interpret because each one is traceable to written intent.

In practice, the sequence looks like this:

1. Draft the Analog Regression Sheet and update relevant natural language oracles.
2. Fill in the regression test case matrix to identify high-risk flows.
3. Use this to guide which tests you write or update.
4. Run the automated suite.
5. Investigate failures with direct reference to your paper-first artifacts.

The result is fewer surprises in CI and a smoother debugging process when something does fail.

Surfacing Ambiguity and Design Flaws Early

Perhaps the most underrated benefit of analog regression is what it reveals about your requirements and design.

When you’re forced to write down:

• “What should happen?”
• “What must never happen?”
• “Under what conditions does this behavior change?”

…you quickly find that many answers are unclear or contradictory.

This is a feature, not a bug. The paper-first routine:

• Exposes ambiguous requirements that would otherwise turn into production incidents.
• Highlights design flaws (e.g., inconsistent behaviors across similar APIs).
• Encourages teams to resolve ambiguity before they embed it in code.

In that sense, the Analog Regression Lab is as much a design-quality practice as a testing practice.

Conclusion: Bringing Analog Discipline to Digital Testing

Modern software teams rely heavily on automated test suites—and they should. But the highest leverage often comes from what happens before you run a single test.

The Analog Regression Lab offers a structured way to bring that discipline into your workflow:

• Paper-first routines to clarify expectations and edge cases.
• Testora, using natural language artifacts as regression oracles.
• Treating requirements, docs, and commit messages as first-class sources of truth.
• A regression test case matrix to focus effort on the riskiest areas.
• Structured checklists to standardize practice and reduce oversight.
• A complementary relationship with automated tests that makes failures more meaningful and easier to debug.

You don’t need to adopt every component at once. Start with one change: for your next feature or bug fix, write the analog regression sheet before you run tests. Capture what you expect to happen, where the risk lies, and what must not break.

Then watch how many issues you catch on paper—before your test suite ever gets the chance.