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Quality Engineering for AI-Assisted Development

If your engineers use Cursor, Claude Code, or GitHub Copilot, your quality function was designed for a rate of change that no longer exists. This page is what we tell VPs of Engineering who ask us what to do about it.

Code Shipped vs. Code Validated After AI Coding Tools Chart showing code shipped per month rising while code validated stays flat, widening the unverified-code gap. before AI coding tools 18 months later unverified code code shipped / week code validated / week
Illustrative. Both series are direct-labeled; the shaded area is the change that reaches production without being exercised.

What Actually Broke

Three things happened at once. Developers started producing more code and producing it in larger, less predictable changes. The tests that ship with AI-generated code are written by the same model that wrote the code, so they confirm the author's assumptions rather than test them. And review capacity, the last human gate, didn't grow at all.

The regression suite becomes the release bottleneck first. Then teams start trimming it to make the release date. Then a defect reaches production that the trimmed suite would have caught, and the organization discovers it no longer knows what its own release process guarantees.

The problem isn't that AI writes bad code. It's that it writes code faster than anyone is checking whether it's good.

What a Redesigned Quality Function Looks Like

  1. Validation Moves Into the Pipeline

    Regression runs on every change, not on the release candidate. Suites are partitioned by risk so the critical path runs in minutes and the long tail runs continuously.

  2. Test Design Is Generated, Then Verified

    Test cases and automation are produced by an AI system from requirements, code diffs, and defect history, and every generated asset is reviewed by an engineer before it enters the suite. Speed from the machine, judgment from the person.

  3. Coverage Is Measured Against Change, Not Against Features

    The question becomes "What fraction of this week's changed surface was exercised before release?"—which is a number an engineering leader can act on.

  4. Maintenance Is Automated

    UI and API drift is detected, and scripts are updated by the system, so the suite doesn't decay into a pile of skipped tests six months after it was built.

  5. The Quality Function Reports Evidence

    A release decision is backed by a written finding: what was validated, what wasn't, and the residual risk. Not a pass count.

What Are Your Options?

You have three realistic options, and we'll be plain about all of them.

  • Rebuild In-House: Right if you have the senior quality engineers to design it and the patience for twelve to eighteen months. The common failure is a better automation framework and the same bottleneck.
  • Buy an AI Testing Tool: Useful for generating tests. Nobody in the vendor's building is accountable for whether your release works, and the maintenance burden stays with you.
  • A quality engineering partner that has redesigned its delivery model for this environment. Look for automation checked into your repository and owned by you, validation running in your CI/CD, pricing tied to change volume rather than headcount, and a written release finding. Ask them what they won't claim. Ask how they price when your change volume doubles. Ask what they had to throw away to get here—a firm that has genuinely re-tooled can tell you, in detail, what it cost them.

This is what QASource has built, for the fourth time in twenty-four years. Here's how it works, and here's what it has produced.

Tell Us What's Actually Breaking

We'll do our homework on your product before the first call, and within two business days of that call you'll have a proposal.

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