By Glue Team

What Is Code Quality Metrics?

Code quality metrics are measurable indicators that show the health and maintainability of software code. They quantify aspects like complexity, test coverage, duplication, and adherence to standards—transforming vague notions of "good code" into concrete, trackable data.

Why Code Quality Matters

Poor code quality creates exponential costs:

• A bug in legacy code takes 5x longer to fix than in well-structured code
• Engineers spend 23-42% of time refactoring poor code instead of building features
• High complexity leads to more bugs, which compounds technical debt
• New engineers take 6 months to be productive in chaotic codebases instead of 6 weeks

Code quality metrics let teams see these costs quantified and track improvement over time.

Key Code Quality Metrics

Cyclomatic complexity: Counts decision paths in code. A function with 15 conditions has higher complexity than one with 2. Complexity > 10 predicts bugs; > 20 predicts maintenance nightmares.

Test coverage: Percentage of code executed by tests. 80%+ coverage correlates with 50% fewer production bugs. Note: coverage measures execution, not test quality; two tests with 80% coverage can have vastly different bug detection rates.

Code duplication: Repeated code is a liability—when you fix a bug in one place, you must find and fix it everywhere it's duplicated. Duplication > 10% signals refactoring need.

Dependency coupling: How many other modules depend on this code? High coupling makes changes risky; low coupling enables rapid iteration.

Code churn: How often does the same code get modified repeatedly? High churn signals unclear requirements or architectural problems.

SLOC per function: Lines of code per function. Functions > 200 LOC are hard to test and understand; < 50 LOC are more maintainable.

Using Metrics to Drive Improvement

Metrics are only valuable if they drive action. Common approaches:

1. Set thresholds: "We accept < 50 LOC per function and > 80% test coverage." New code violating thresholds gets feedback in review.

2. Track trends: Monitor whether cyclomatic complexity is rising (bad) or falling (good). A 5% quarterly improvement is significant.

3. Connect to business impact: When a module with high complexity produces 10x more bugs, leadership understands why refactoring matters.

4. Automate enforcement: CI/CD blocks merges that violate quality thresholds, shifting quality left.

Metrics Anti-Patterns

• Chasing coverage numbers: 100% coverage is not achievable and doesn't predict quality; focus on coverage of critical paths.
• Blame culture: "Your code is too complex" doesn't help; context matters. Help engineers improve, not feel judged.
• Ignoring context: A 200-line mathematical function may be clearer than fragmented logic; use judgment, not rigid thresholds.

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Frequently Asked Questions

Q: What's the difference between code quality and code performance? A: Quality measures maintainability and correctness; performance measures speed and resource use. Good quality code can have poor performance, and vice versa.

Q: Should we refactor just to improve metrics? A: No. Refactor when metrics reveal actual risks: high complexity in code that changes frequently, low coverage in critical paths, or duplication causing bugs.

Q: How often should we measure code quality? A: Continuously. Metrics should be visible in dashboards, updated after each merge, so trends are visible and improvements are rewarded in real-time.