Tree-sitter is a powerful incremental parsing library used to analyze and manipulate source code. It is widely employed in text editors, IDEs, and developer tools to provide fast and accurate syntax tree parsing. In this article, we will explore how to use Tree-sitter in Python to build a custom tool for parsing source code and extracting call graphs.

Introduction to Tree-Sitter

Tree-sitter is an efficient, incremental parsing library designed to build and process syntax trees. It supports multiple programming languages and allows users to write custom queries to analyze and extract information from source code. Some of its key features include:

• Incremental parsing: Efficiently updates parse trees as code changes.
• Multi-language support: Includes grammars for languages like JavaScript, Python, C, and more.
• Custom queries: Uses Tree-sitter’s query language to extract meaningful information from syntax trees.
• Speed and accuracy: Provides fast parsing with detailed syntax representations.

In this guide, we will leverage Tree-sitter in Python to extract call graphs from source code. A call graph represents function calls within a program and helps in understanding code structure, dependencies, and flow.

Installing Tree-Sitter in Python

Before we begin, we need to install the necessary dependencies. You can install the tree-sitter package via pip:

pip install tree-sitter

Additionally, we need to install language grammars. For example, to parse Python source code, we will use the Python grammar from the official Tree-sitter repository.

Setting Up Tree-Sitter with Python

To use Tree-sitter in Python, follow these steps:

1. Clone the Tree-sitter grammar for Python:

git clone https://github.com/tree-sitter/tree-sitter-python.git

2. Compile the grammar and load it in Python:

from tree_sitter import Language, Parser

# Build the language library
Language.build_library(
    'build/my-languages.so',
    ['tree-sitter-python']
)

# Load the compiled language
PYTHON_LANGUAGE = Language('build/my-languages.so', 'python')

# Create a parser
parser = Parser()
parser.set_language(PYTHON_LANGUAGE)

Now, we have set up Tree-sitter for parsing Python code.

Parsing Source Code and Extracting Function Calls

To extract function calls, we will parse Python source code and use Tree-sitter queries to identify function definitions and call sites.

Example Code to Parse Python Functions

def parse_python_code(source_code):
    tree = parser.parse(bytes(source_code, "utf8"))
    return tree

code_sample = """
def foo():
    print("Hello, World!")

def bar():
    foo()
    print("Inside bar")

bar()
"""

syntax_tree = parse_python_code(code_sample)
print(syntax_tree.root_node.sexp())

This will output a syntax tree representation of the given Python code.

Extracting Function Calls using Tree-Sitter Queries

Tree-sitter provides a query system to search for specific syntax patterns within the parse tree. We will use it to find function definitions and calls.

Defining a Tree-Sitter Query to Find Function Calls

from tree_sitter import Node

def get_function_calls(node: Node, source_code: str):
    calls = []
    if node.type == "call":
        call_name = source_code[node.start_byte:node.end_byte]
        calls.append(call_name)
    for child in node.children:
        calls.extend(get_function_calls(child, source_code))
    return calls

calls = get_function_calls(syntax_tree.root_node, code_sample)
print("Function Calls:", calls)

This script will extract function calls such as foo() and bar() from the given source code.

Building a Call Graph

Now that we can extract function calls, we can represent them in a call graph. A call graph is a directed graph where nodes represent functions and edges represent calls between them.

Representing the Call Graph using NetworkX

import networkx as nx
import matplotlib.pyplot as plt

def build_call_graph(source_code):
    tree = parse_python_code(source_code)
    graph = nx.DiGraph()
    
    functions = {}
    
    def find_functions(node, source_code):
        if node.type == "function_definition":
            func_name = source_code[node.child_by_field_name("name").start_byte : node.child_by_field_name("name").end_byte]
            functions[func_name] = node
            graph.add_node(func_name)
        for child in node.children:
            find_functions(child, source_code)
    
    find_functions(tree.root_node, source_code)
    
    for func_name, node in functions.items():
        calls = get_function_calls(node, source_code)
        for call in calls:
            graph.add_edge(func_name, call)
    
    return graph

def visualize_call_graph(graph):
    plt.figure(figsize=(8,6))
    pos = nx.spring_layout(graph)
    nx.draw(graph, pos, with_labels=True, node_color='lightblue', edge_color='gray')
    plt.show()

call_graph = build_call_graph(code_sample)
visualize_call_graph(call_graph)

This script extracts function definitions and calls, constructs a call graph, and visualizes it using NetworkX and Matplotlib.

Applications of Call Graphs

Building call graphs can be useful in various scenarios:

• Code analysis and refactoring: Understanding dependencies between functions helps in optimizing and refactoring large codebases.
• Security auditing: Identifying functions that call unsafe methods can help in finding security vulnerabilities.
• Performance optimization: Detecting redundant or expensive function calls can assist in optimizing execution time.
• Automated documentation: Generating visual call graphs can improve documentation by showcasing function interactions.

Conclusion

In this article, we explored how to use the Tree-sitter library in Python to parse source code and extract call graphs. We started with the installation and setup of Tree-sitter, parsed Python source code, and used Tree-sitter queries to extract function calls. Finally, we built and visualized a call graph using NetworkX.

Tree-sitter is a powerful tool for syntax analysis, enabling developers to build custom tools for code analysis, refactoring, and visualization. With its efficiency and versatility, it can be integrated into various applications to enhance code understanding and maintainability.