Traversing the Call Graph

Codegen provides powerful capabilities for analyzing and visualizing function call relationships in your codebase. This guide will show you how to traverse the call graph and create visual representations of function call paths.

Understanding Call Graph Traversal

At the heart of call graph traversal is the .function_calls property, which returns information about all function calls made within a function:

def example_function():
    result = helper_function()
    process_data()
    return result

# Get all calls made by example_function
successors = example_function.function_calls
for successor in successors:
    print(f"Call: {successor.source}")  # The actual function call
    print(f"Called: {successor.function_definition.name}")  # The function being called

Building a Call Graph

Here’s how to build a directed graph of function calls using NetworkX:

import networkx as nx
from codegen.sdk.core.interfaces.callable import FunctionCallDefinition
from codegen.sdk.core.function import Function

def create_call_graph(start_func, end_func, max_depth=5):
    G = nx.DiGraph()

    def traverse_calls(parent_func, current_depth):
        if current_depth > max_depth:
            return

        # Determine source node
        if isinstance(parent_func, Function):
            src_call = src_func = parent_func
        else:
            src_func = parent_func.function_definition
            src_call = parent_func

        # Traverse all function calls
        for call in src_func.function_calls:
            func = call.function_definition

            # Skip recursive calls
            if func.name == src_func.name:
                continue

            # Add nodes and edges
            G.add_node(call)
            G.add_edge(src_call, call)

            # Check if we reached the target
            if func == end_func:
                G.add_edge(call, end_func)
                return

            # Continue traversal
            traverse_calls(call, current_depth + 1)

    # Initialize graph
    G.add_node(start_func, color="blue")  # Start node
    G.add_node(end_func, color="red")     # End node

    # Start traversal
    traverse_calls(start_func, 1)
    return G

# Usage example
start = codebase.get_function("create_skill")
end = codebase.get_function("auto_define_skill_description")
graph = create_call_graph(start, end)

Filtering and Visualization

You can filter the graph to show only relevant paths and visualize the results:

# Find all paths between start and end
all_paths = nx.all_simple_paths(graph, source=start, target=end)

# Create subgraph of only the nodes in these paths
nodes_in_paths = set()
for path in all_paths:
    nodes_in_paths.update(path)
filtered_graph = graph.subgraph(nodes_in_paths)

# Visualize the graph
codebase.visualize(filtered_graph)

Advanced Usage

Example: Finding Dead Code

You can use call graph analysis to find unused functions:

def find_dead_code(codebase):
    dead_functions = []
    for function in codebase.functions:
        if not any(function.function_calls):
            # No other functions call this one
            dead_functions.append(function)
    return dead_functions

Example: Analyzing Call Chains

Find the longest call chain in your codebase:

def get_max_call_chain(function):
    G = nx.DiGraph()

    def build_graph(func, depth=0):
        if depth > 10:  # Prevent infinite recursion
            return
        for call in func.function_calls:
            called_func = call.function_definition
            G.add_edge(func, called_func)
            build_graph(called_func, depth + 1)

    build_graph(function)
    return nx.dag_longest_path(G)

The .function_calls property is optimized for performance and uses Codegen’s internal graph structure to quickly traverse relationships. It’s much faster than parsing the code repeatedly.

When traversing call graphs, be mindful of:

• Recursive calls that could create infinite loops
• External module calls that might not be resolvable
• Dynamic/runtime function calls that can’t be statically analyzed