Import loops

Circular dependencies in the codebase can lead to several issues in software development.

Complexity and Confusion

Understanding Code: Circular dependencies can make it difficult for developers to understand the relationships between different modules or files. When two or more files depend on each other, it can create a tangled web of dependencies that is hard to follow. Maintenance Challenges: As the codebase grows, maintaining and modifying code with circular dependencies can become increasingly complex. Developers may struggle to determine which module to modify or how changes in one module will affect others.

Import Errors

Runtime Errors: In many programming languages, circular imports can lead to runtime errors. For example, if two modules import each other, one of them may not be fully initialized when the other tries to use it, leading to ImportError or AttributeError. Initialization Issues: When a module is imported, its top-level code is executed. If that code relies on another module that is also trying to import it, it can lead to incomplete initialization and unexpected behavior.

Testing Difficulties

Isolation of Tests: Circular dependencies can complicate unit testing. If modules are tightly coupled due to circular imports, it becomes harder to isolate them for testing, leading to tests that are more difficult to write and maintain. Mocking Challenges: When testing, mocking dependencies can become cumbersome if there are circular references, as it may require more complex setups to ensure that all dependencies are properly accounted for.

Refactoring Difficulties

Code Refactoring: Circular dependencies can hinder refactoring efforts. When trying to reorganize or improve the structure of the code, developers may find it challenging to break the cycle without introducing additional complexity or errors. Code Reusability: Circular dependencies can limit the reusability of modules. If a module is tightly coupled with another due to circular imports, it may not be easily reusable in other contexts or projects.

Performance Implications

Import Overhead: Circular dependencies can lead to unnecessary import overhead, as modules may be loaded multiple times or in a non-optimal order, potentially affecting performance.

Codemod

The following codemod goes through all the imports in the codebase and identifies any circular dependencies. It then visualizes the import cycles in the codebase.

G: DiGraph = networkx.DiGraph()

# iterate over all imports
for pyimport in codebase.imports:
    # Extract to/from files
    if pyimport.from_file and pyimport.to_file:
        # Add nodes and edges to the graph
        G.add_edge(pyimport.from_file.file_path, pyimport.to_file.file_path)

# Find strongly connected components
strongly_connected_components = list(networkx.strongly_connected_components(G))

# Count the number of cycles (SCCs with more than one node)
import_cycles = [scc for scc in strongly_connected_components if len(scc) > 1]

print(f"Found {len(import_cycles)} import cycles")

# Visualize the import cycles
# Create a new graph for the cycle nodes
cycle_graph: DiGraph = networkx.DiGraph()

# Add nodes involved in cycles to the new graph
for cycle in networkx.simple_cycles(G):
    if len(cycle) > 2:
        # Add nodes to the cycle graph
        for node in cycle:
            cycle_graph.add_node(node)
        # Add edges between the nodes in the cycle
        for i in range(len(cycle)):
            cycle_graph.add_edge(cycle[i], cycle[(i + 1) % len(cycle)])  # Connect in a circular manner
        # Depends on the size of the codebase and the number of cycles, this may take a while to run
        # so we break after the first cycle it's found
        break

codebase.visualize(cycle_graph)