Code quality metrics are quantitative measurements used to assess various aspects of software code. These metrics provide objective data about code characteristics, helping teams identify potential issues, track improvements, and make informed decisions about refactoring and maintenance priorities.
Coverage Metrics
Coverage metrics measure how much of your code is exercised by tests:
- Line Coverage: Percentage of code lines executed during tests
- Branch Coverage: Percentage of possible code branches (if/else paths) executed
- Function Coverage: Percentage of functions or methods called during tests
- Statement Coverage: Percentage of executable statements executed
- Patch Coverage: Coverage specifically for changed lines in a commit or PR
- Condition Coverage: Percentage of boolean subexpressions evaluated to both true and false
Complexity Metrics
Complexity metrics measure how intricate and potentially difficult to understand code is:
- Cyclomatic Complexity: Number of independent paths through code
- Cognitive Complexity: Measure of how difficult code is to understand mentally
- Weighted Method Count: Sum of complexities for all methods in a class
- Nesting Depth: Maximum levels of nested control structures
- Essential Complexity: Complexity remaining after structured programming constructs are considered
Size and Volume Metrics
Size metrics quantify the amount of code:
- Lines of Code (LOC): Total number of lines in a file or module
- Logical Lines of Code (LLOC): Number of actual statements
- Comment Density: Ratio of comments to code
- Method Length: Number of lines or statements in methods
- Class Size: Number of methods or properties in a class
- File Length: Number of lines in a source file
Object-Oriented Metrics
Object-oriented metrics assess class and object relationships:
- Coupling: Dependencies between classes (afferent and efferent coupling)
- Cohesion: How closely elements within a module are related
- Inheritance Depth: Levels of inheritance hierarchy
- Number of Children: Count of direct subclasses
- Response for a Class: Count of methods that can be executed in response to a message
- Lack of Cohesion in Methods: Measure of method/attribute interactions
Maintainability Metrics
Maintainability metrics predict how easy code will be to change:
- Maintainability Index: Combined metric based on complexity, volume, and comments
- CRAP Score: Change Risk Anti-Patterns score combining complexity and coverage
- Technical Debt Ratio: Estimated effort required to fix all quality issues
- Code Churn: Frequency and extent of code changes over time
- Refactoring Candidates: Count of identified code smells
Reliability Metrics
Reliability metrics assess code stability and robustness:
- Defect Density: Number of defects per unit of code size
- Defect Resolution Time: Time required to fix reported issues
- Test Case Density: Number of test cases relative to code size
- Mutation Score: Percentage of code mutations detected by tests
- Exception Handling Ratio: Proportion of code with proper exception handling
Other Specialized Metrics
Additional metrics for specific aspects of code quality:
- Duplicate Code (Clone Detection): Percentage of repeated code patterns
- Comment Quality: Assessment of documentation completeness and clarity
- Dependency Freshness: How current library dependencies are
- Security Vulnerability Count: Number of potential security issues
- Dead Code: Amount of code that's never executed
Interpreting Code Quality Metrics
To use metrics effectively:
- Consider Context: Different types of code and projects have different acceptable metric ranges
- Use Multiple Metrics: No single metric provides a complete picture of quality
- Track Trends: Changes in metrics over time often matter more than absolute values
- Set Reasonable Thresholds: Establish thresholds based on project needs and maturity
- Balance Metrics: Sometimes optimizing one metric may negatively impact another
Measuring Code Quality with OtterWise
OtterWise provides tools to track and analyze several key code quality metrics:
- Coverage Metrics: Line, branch, and patch coverage percentages
- Complexity Analysis: Cyclomatic complexity measurements for methods and files
- CRAP Score Calculation: Combined risk assessment based on complexity and coverage
- Historical Trending: Tracking of metrics over time to identify improvements or regressions
- Pull Request Analysis: Quality metrics specifically for changed code
Code quality metrics provide an objective foundation for assessing and improving software. By tracking these measurements systematically, development teams can identify areas for improvement, validate refactoring efforts, and gradually enhance overall code quality. While metrics should never replace developer judgment, they offer valuable data to support informed decisions about code maintenance and improvement.
