What metrics help measure maintainability, and what are their limitations?

• A. Cyclomatic complexity, coupling, cohesion, and code churn; metrics guide improvements but don’t replace code judgment
• B. Only lines of code
• C. Runtime performance metrics
• D. Memory usage metrics

Answer: (A)

Maintainability is best measured by a mix of structural and change-related metrics that reveal how easy code is to understand, modify, and extend. Cyclomatic complexity examines how many independent paths exist through a piece of code, which relates to how hard it is to test and reason about. Lower complexity generally means clearer logic and fewer scenarios to consider when making changes. Coupling looks at how much one module relies on others; looser coupling means changes in one area are less likely to cause ripple effects elsewhere, making maintenance safer and more straightforward. Cohesion assesses how focused a module’s responsibilities are; high cohesion means a module does one thing well, making it easier to understand, reuse, and maintain. Code churn tracks how often code is modified over time; areas with frequent changes often signal instability or parts that require more attention, tests, or redesign.

These metrics together give a practical picture of maintainability, guiding improvements rather than dictating them. They help highlight where complexity, interdependencies, or instability are making maintenance harder, while acknowledging that other important factors—like readability, documentation, test coverage, and architectural decisions—also influence maintainability. Relying only on lines of code or on runtime/memory performance metrics misses the maintenance-focused signals that the chosen set captures, so those other metrics don’t serve as direct measures of maintainability.