Technical Product Management Course · by Stanislav Belyaev
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Code Coverage

3 outgoing · 1 incoming · 4 total connections

Map Detail
Testing & Quality

Code Coverage

Code Coverage measures the percentage of source code lines, branches, or paths exercised by the automated test suite. While not a direct measure of test quality, it highlights untested areas where regressions could go undetected. Teams use coverage trends to ensure that new code is accompanied by tests and that critical paths maintain adequate verification.

Percentage of code exercised by tests. Google targets: 60% / 75% / 90%.

MONOREPO CONTEXT

Similar dynamics. Monorepos make coverage measurement easier (unified tooling) but the absolute number of tests is larger. Coverage of shared libraries becomes critical since they affect many consumers.

Scale Impact
👤 Solo / Pair (1–3)
0.5
👥 Team (4–15)
0.6
🏢 Department (15–100)
0.7
🏛️ Organization (100+)
0.8

Relatively stable across scales, but shared library coverage becomes critical at scale since low coverage in core modules affects many consumers.

3
Influences
1
Influenced by

→ Influences

Change Failure Rate (CFR)

Higher coverage catches more bugs pre-production.

Google thresholds
Google + industry standards
Technical Debt

Coverage enables safe refactoring.

Prerequisite for refactoring
Testing best practices
AI Security Vuln Rate

Strong test coverage catches AI security flaws before production. DORA #1 success factor.

Safety net for AI code
Security Research Best Practices

← Influenced by

Test Flakiness

Teams disable flaky tests, creating persistent coverage gaps.

Common in large codebases
Gradle + Atlassian research
Metrics map by Stanislav Belyaev · Analysis powered by Anthropic Claude Opus 4.6 · All data validated by human experts