Are Abstractness, Instability, and Distance Metrics Meaningful for Python?

No metric can replace experience, refactoring, and understanding the system’s goals. Metrics are useful for spotting unusual coupling or poor modularity, but they should prompt questions rather than force a particular architecture. Read the concepts critically, keep what helps, and validate the conclusions against maintainability and actual development problems.

The underlying ideas are more valuable than the exact formulas. Architecture always involves trade-offs, so learn what the metrics are trying to reveal and adapt them to the project. A large, cohesive module—such as a dataframe implementation with many related operations—might be perfectly healthy even if a simplistic size-based measure flags it.

The metrics can still be useful if you treat them as indicators rather than hard rules. In Python, import and dependency graphs are often a more practical way to think about coupling than formal interfaces. Use the results to find areas worth investigating, not to declare a design objectively good or bad.

The implementation seems reasonable as a visualization and analysis tool, but its output needs context. Python’s dynamic behavior means static analysis cannot see every runtime dependency or contract. Type hints and tools such as mypy, pylint, or flake8 can provide additional signals, while code review and tests are still essential.