Tests for AI-Assisted Code
Test strategy and implementation for AI-assisted projects - covering what to test (and what AI tools cannot test for you), what to mock, what to skip, and how to keep regressions in check as the AI keeps generating code.
Entry packageTest Strategy Audit: 1 Repositoryfrom EUR 3,8001 repository · up to 3 weeks from scopingWhat is includedTests that protect behaviour
The audit separates tests that guard what matters from tests that only inflate the coverage number.
Critical paths covered by hand
The tests AI tools generate badly are written by engineers and wired into CI.
A strategy you can apply
What to test, what to mock, what to skip, written down and tied to your product risk.
23Minds designs and implements test coverage strategies for engineering teams shipping AI-assisted code.
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AI coding tools generate tests that look thorough but often miss regressions and prop-up coverage metrics without protecting behavior. The service audits current tests, designs a coverage strategy aligned to your product risk (what must never break vs what can fail and be fixed forward), implements the critical-path tests AI cannot generate well, and sets up the CI signals that catch real regressions early.
What makes this service defensible.
- Audit before any new test
The existing suite is scored first, so the strategy answers what is actually missing.
- Up to 20 critical-path tests in the package
Implemented, reviewed and running in your CI before handover.
- Flake and regression signals in CI
The pipeline reports failure modes, not only red and green.
- Prompt patterns for your team
A playbook for generating good tests with AI, so the suite keeps growing after the engagement.
The process, phase by phase.
Pick the way in that fits your budget and timing.
Every practice has a free first step, an entry package with a written deliverable and its price listed here, and a full project or retainer quoted after a first call. Once the scope is agreed, the price is fixed, with no hidden costs.
- 1Step 1 · Free
Intro call or self-assessment
60 minutes · 1 engineer · no obligation
FreeTalk to an engineerShow details
- Intro call with an engineer, or the online self-assessment
- A written next step within one business day
- No obligation and no sales deck
- Recommended2Step 2 · Entry package
Test Strategy Audit: 1 Repository
1 repository · up to 3 weeks from scoping
from EUR 3,800 netThe price is fixed once the scope is agreed on the intro call, no hidden costs; extras only from the Not included list.
Ask for this packageShow details
- Audit of the existing test suite in one repository
- Written test strategy aligned with risk
- Up to 20 critical-path tests implemented
- Handover session for the team
- Not included: Reaching a target coverage threshold, full-scope performance and E2E tests, CI pipeline rebuild, ongoing test maintenance.
- 3Step 3 · Project or retainer
Full scope, quoted after a first call
Full scope · phased delivery · quoted per phase
from EUR 18,700 net, quoted after a first callStart the conversationShow details
- Full test rebuild across the platform
- CI failure-mode signals
- Team enablement on the new suite
The situations that bring teams to this service.
- AI generates tests that pass but do not protect business-critical behavior
- Coverage metrics rise but customer-reported regressions also rise
- Mocks proliferate as AI fills in gaps; the test suite tests itself, not the system
Show the other problems (1)
- Critical-path integration tests are too tedious for AI to generate well; nobody writes them
- Engineering managers whose AI-assisted code is shipping faster than tests can keep up
- Tech leads watching test coverage metrics rise while regressions rise too
- CTOs trying to scale a small team using AI without breaking quality
- QA leads adjusting their strategy for AI-generated code
Where this goes wrong, and what we do about it.
1. Coverage becomes the goal and stops meaning anythingWhat stays with you: list of deliberate breakages the suite failed to catch, and the rewritten tests that now catch them
What happensAsked how to test AI-written code, the most common answer is to have the assistant write unit tests to near-total coverage, and the next reply asks how you test the interface. Generated tests tend to assert what the code does rather than what it should do, so a high number certifies the implementation against itself and a whole class of defects goes unnoticed.
What we do about itWe check what the tests would actually catch by breaking the code on purpose and seeing which tests fail. Mutation-style probing on the paths that matter tells us where the suite is decorative, and we rewrite those tests against the specification rather than the implementation.
2. The parts nobody automates are the parts that breakWhat stays with you: written coverage strategy with the decision and reason per seam, plus the integration tests for the ones worth automating
What happensAssistants generate unit tests readily and integration tests reluctantly, so suites grow deep and narrow. The failures that reach users come from the seams: a contract that changed, a migration that ran out of order, a third party that answered differently. None of those live inside a unit test.
What we do about itWe decide what to test, what to stub and what to leave alone, in that order, and we write it down with the reason for each. Where a seam cannot be covered cheaply, we say so and propose monitoring instead of a test that pretends.
3. The suite slows down until it gets skippedWhat stays with you: before and after runtime, the list of removed tests with justification, and a quarantine policy the team can apply itself
What happensGenerated tests are cheap to add and nobody removes them, so runtime grows, flakiness grows with it, and the team starts merging on red because the red is usually noise. At that point the suite costs money and buys nothing.
What we do about itWe measure runtime and flakiness per test, delete what duplicates other coverage, and quarantine what is genuinely unstable instead of letting it teach the team to ignore failures.
Deliverables, not deliverable-shaped slides.
- Audit of current test suite - what protects behavior vs what just inflates metrics
- Risk-aligned test strategy document (what to test, what to mock, what to skip)
- Implementation of critical-path tests AI tools struggle to generate
- CI configuration for failure-mode signals (flake detection, regression alerts)
- Playbook for team: prompt patterns for generating good tests with AI
Questions procurement teams ask.
Guides that lead to this service.
Talk to an engineer.
Describe where you are with tests for AI-assisted code. A reply within one business day.