component-boundary-identifier

To identify microservice boundaries in a monolithic codebase, this Skill analyzes direct calls, imports, shared data types, and co-change history to build a weighted dependency graph. It then applies clustering algorithms to find natural component divisions for extraction planning.

Analyzing code dependencies for modularization requires evaluating cohesion and coupling metrics across your codebase. This Skill weights edges based on shared database tables, configuration, and imports, then employs Louvain or spectral clustering methods to propose optimal component boundaries.

Clustering a dependency graph helps with code modularization by algorithmically grouping highly interconnected code modules. This Skill evaluates internal strength and external dependencies to flag clean, extractable components while highlighting poorly defined areas needing restructuring before microservice extraction.

Yes, you can use dependency analysis to plan microservice extraction for large applications. By analyzing the import graph and co-change history, this Skill quantifies coupling and cohesion metrics to determine which specific parts of a monolithic application can be safely extracted into independent deployments.

Evaluating cohesion and coupling metrics for software architecture refactoring requires data on direct calls, imports, shared data types, database tables, configuration, and co-change history. This Skill uses these inputs to weight edges in a dependency graph and assess the viability of proposed component boundaries.

You should avoid using dependency clustering for code modularization when your codebase lacks sufficient direct calls, shared data types, or co-change history to build a reliable weighted dependency graph. The Skill requires these interconnected data points to accurately evaluate cohesion and propose component boundaries.