QA Metrics

<objective> A suite with 4,000 tests where 800 are disabled and 200 are flaky looks healthy on a slide and lies in production. This skill defines the metrics that actually change behavior — each one with a formula, a target, an owner, and a concrete action when it goes red — so the dashboard becomes a feedback loop instead of decoration. </objective>

Quick Route

Discovery Questions

Check .agents/qa-project-context.md first — if it exists, use it as the foundation and skip anything already answered there. Then:

Current State

• What metrics are you tracking today? (Even "we glance at CI pass rate sometimes" counts.)
• Where does your test data live? (CI system, coverage reports, bug tracker, spreadsheets, nowhere)
• Do you have any dashboards already? Who looks at them, and how often?
• What tooling runs your CI pipeline? (GitHub Actions, GitLab CI, Jenkins, CircleCI)

Stakeholders

• Who are the stakeholders for quality metrics? (engineering, product, leadership, customers)
• What does each care about? Engineers want flakiness; leadership wants escape trends; product wants release confidence.
• Who will own each metric? If nobody owns it, nobody acts on it.

Quality Problems

• What quality problems need visibility? (regressions, slow pipelines, flaky tests, coverage gaps, incidents)
• What broke in production recently? Would a metric have caught it earlier?
• What decisions are you making without data today?

Goals

• What does "healthy test suite" mean for your team?
• Any compliance or contractual quality requirements? (SLAs, SOC2, ISO)
• Quick wins vs. full observability — what is the appetite for metrics infrastructure?

Core Principles

1. Metrics Should Drive Action, Not Just Dashboards

A metric without an action plan is decoration. For every metric, define: what threshold triggers action, what the action is, and who takes it. If flakiness crosses 5%, the on-call engineer investigates the top 3 flaky tests that week. No ambiguity.

2. Leading vs Lagging — Track Both, Act on Leading

Defect escape rate and MTTR are lagging: you learn after users were hurt. Flakiness, coverage delta, and skipped-test count are leading: they predict escapes before they happen. Lagging metrics tell leadership whether quality is moving; leading metrics are where engineers spend their daily attention because they can still change the outcome.

3. Trend Over Snapshot

A single number is nearly useless. Coverage at 72% means nothing; coverage trending 68% → 72% over three sprints tells a story. Display metrics as time series and evaluate direction, not absolute position.

4. Every Metric Needs a Target and an Owner

A target makes a metric actionable; an owner makes it accountable. Without both it becomes background noise. Set targets from your team's maturity (see the targets table) and assign owners who can actually move the number.

5. Vanity Metrics Waste Everyone's Time

"We have 4,000 tests" sounds impressive until 800 are disabled and 200 flaky. Count what matters: tests that run, pass reliably, and catch real bugs. The ultimate metric is whether users hit bugs that hurt the business — work backward from there. Defect escape rate connects directly to user experience; lines of test code connects to nothing.

Essential QA Metrics

Each metric: definition, formula, recommended target, why it matters, and the action when it goes red.

Test Coverage Metrics

How much of our system is verified by automated tests? (leading)

Code Coverage Percentage

The percentage of code exercised by automated tests (line, branch, or statement level).

Line coverage   = (lines executed by tests / total executable lines) × 100
Branch coverage = (branches executed by tests / total branches) × 100

Targets: Line: 70-85% for app code. Branch: 60-75%. Critical paths (payments, auth): 90%+.

Why it matters: Coverage identifies blind spots — code never exercised by tests is where bugs hide undetected.

When it goes red: Coverage drops on PR: block merge or flag. Low in critical module: create targeted tasks. Plateaus: check for dead code vs. genuinely untested logic.

Warning: Coverage measures execution, not assertion quality. Pair it with mutation testing (StrykerJS v9.6+, mutmut) for a truer picture. Stryker's Vitest runner tracks recent Vitest releases — check the runner's peer-dependency before pinning a Vitest version. Use incremental: true in monorepo CI to keep mutation runs cheap. For deep coverage/mutation analysis, see coverage-analysis.

Requirement Coverage Percentage

Requirement coverage = (features with at least one test / total features) × 100

Targets: 100% for P0/P1 features, 80%+ for P2.

Why it matters: A feature can have zero tests even if surrounding code is well-covered. Use test tags (@feature:checkout, @story:PROJ-1234) for traceability.

Risk Coverage Percentage

Risk coverage = (high-risk areas with automated tests / total high-risk areas) × 100

Targets: 95%+ for high-risk areas. Maintain a risk register and cross-reference against coverage data per module.

Test Health Metrics

Can we trust our test suite? (leading)

Flakiness Rate

The percentage of test runs producing inconsistent results without code changes.

Flakiness rate = (test runs with flaky results / total test runs) × 100

Targets: Acceptable: <2%. Warning: 2-5%. Critical: >5%.

Why it matters: Flaky tests erode trust. Once developers think "probably just flaky," they stop paying attention to results at all. Flakiness is the single biggest threat to a suite's credibility.

When it goes red: Quarantine flaky tests immediately. Investigate the top 3 weekly — most flakiness comes from a small number of tests. Common causes: timing/race conditions, shared state, external dependencies, order-dependent tests. Tests flaky for 30+ days should be deleted or rewritten.

Detection: Buildkite Test Analytics, Datadog Test Optimization (formerly Datadog CI Visibility — ships Flaky Test Management, Auto Test Retries, Early Flake Detection, Failed Test Replay, and Test Impact Analysis; its Bits AI Dev Agent can auto-open PRs to fix flaky tests), Trunk Flaky Tests, or a script comparing results across runs on the same commit.

Pass Rate Trend (7-Day Rolling)

Pass rate = (green CI runs / total CI runs) × 100  [rolling 7 days]

Targets: Healthy: >95%. Warning: 90-95%. Broken: <90%.

The 7-day rolling average smooths daily noise and reveals the real trend. A consistently red build means developers ignore the pipeline.

Disabled and Skipped Test Count

Total tests marked skip, disabled, pending, xit, xdescribe or equivalent.

Target: Trend toward zero. Skipped tests older than 2 sprints: fix or delete. Add a CI step that fails if skipped count exceeds 5% of total tests.

Why it matters: Skipped tests are invisible coverage gaps. A suite with 500 passing and 150 skipped tests has a 150 / (500 + 150) = 23% gap that dashboards hide.

Test Suite Duration

Wall-clock time from suite start to completion.