02 - Evaluate & Secure / Test Coverage

Test Coverage

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.

Talk to an engineer How we work

Summary for AI assistants & procurement teams

dfzoo AI Institute designs and implements test coverage strategies for engineering teams shipping AI-assisted code. AI coding tools generate tests that look thorough but often miss regressions and prop-up coverage metrics without protecting behavior. We audit current tests, design a coverage strategy aligned to your product risk (what must never break vs what can fail and be fixed forward), implement the critical-path tests AI cannot generate well, and set up the CI signals that catch real regressions early.

Who it’s for

Built for teams in these situations.

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

Problems we solve

The triggers that bring clients in.

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
Critical-path integration tests are too tedious for AI to generate well; nobody writes them

What you get

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

How we work

The process, phase by phase.

1
1. Test audit
Sample existing tests across modules. Score each for true behavior protection vs metric padding. Identify gaps in critical paths.
Week 1
2
2. Strategy design
Map product risk to test strategy. Decide unit/integration/e2e mix. Pick what to mock vs run for real.
Week 2
3
3. Implementation
Write the critical-path tests AI tools miss. Set up CI signals. Document the prompt patterns for AI-generated tests.
Week 3-4
4
4. Team handover
Walk the team through the strategy. Pair on writing new tests using the playbook. Establish a review cadence.
Week 4-5

How to start

Three ways in. Pick the one that fits your budget and timing.

Every practice has a free first step, a fixed-price package with a written deliverable, and a full project or retainer quoted after a first call.

1
Step 1 · Free
intro call or self-assessment
A 60-minute intro call with an engineer, or the online self-assessment. You leave with a clear next step, no obligation.
Free
Talk to an engineer
2
Step 2 · Fixed price
Test Strategy Audit: 1 Repository
Audit of the existing test suite in one repository, a written risk-aligned test strategy, and implementation of up to 20 critical-path tests.
from EUR 3,800 net, fixed-price package
Not included: Reaching a target coverage threshold, full-scope performance and E2E tests, CI pipeline rebuild, ongoing test maintenance.
Ask for this package
3
Step 3 · Project or retainer
Full scope, quoted after a first call
Full test rebuild across the platform with CI failure-mode signals: from 18 700 EUR.
Quoted after a first call
Talk to us

FAQ

Questions procurement teams ask.

AI is great at unit tests that exercise visible function signatures, less good at integration tests that depend on system state, and weak at end-to-end tests where the cost of correct fixture setup exceeds what fits in context. The result is a test suite skewed toward easy wins, weak on real risk.

We optimize for behavior protection, not raw coverage percentage. Most clients see coverage go down slightly (we remove tests that did not protect anything) and customer-reported regression rate drop more.

Yes. GitHub Actions, GitLab CI, CircleCI, Jenkins, Buildkite - we work with what you have. We do not introduce new CI tools without a strong reason.

TypeScript / JavaScript, Python, Go, Ruby, Java/Kotlin. Mobile (Swift, Kotlin) and embedded are out of scope for this practice.

AI Code Evaluation audits code for correctness; Test Coverage builds the system that catches new regressions before they reach production. Most clients buy them as a pair.

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