How to Build a Tool to Map Codebases from the Source – Automating App Architecture Diagrams

In the software development lifecycle, understanding the architecture of an application is crucial. However, maintaining up-to-date architecture diagrams can be a challenging and time-consuming task. Automating this process can greatly enhance productivity and ensure that architecture diagrams are always accurate and up-to-date. This article will guide you through the process of building a tool to map codebases from the source, creating automated application architecture diagrams.

Understanding the Need for Automated Architecture Diagrams

Manual creation and maintenance of architecture diagrams can lead to several issues:

Inaccuracy: As the codebase evolves, keeping diagrams updated becomes increasingly difficult.
Time-consuming: Regular updates require significant effort, diverting resources from actual development.
Miscommunication: Outdated diagrams can lead to misunderstandings and misaligned development efforts.

An automated tool can resolve these issues by continuously analyzing the codebase and generating accurate, up-to-date diagrams.

Key Components of the Tool

Building an automated architecture diagram tool involves several key components:

Code Parsing: Analyzing the codebase to extract structural information.
Data Storage: Storing the extracted information in a structured format.
Diagram Generation: Converting the stored data into visual diagrams.
User Interface: Providing a user-friendly interface to view and interact with the diagrams.

Code Parsing

Code parsing is the first step, where the tool analyzes the source code to understand its structure. This involves identifying modules, classes, functions, and their relationships. For this example, we’ll use Python’s ast module to parse a Python codebase.

python

import ast

class CodeParser(ast.NodeVisitor):
def __init__(self):
self.classes = []
self.functions = []
self.imports = []

def visit_ClassDef(self, node):
self.classes.append(node.name)
self.generic_visit(node)

def visit_FunctionDef(self, node):
self.functions.append(node.name)
self.generic_visit(node)

def visit_Import(self, node):
for alias in node.names:
self.imports.append(alias.name)
self.generic_visit(node)

def visit_ImportFrom(self, node):
for alias in node.names:
self.imports.append(f”{node.module}.{alias.name}“)
self.generic_visit(node)

def parse_code(source_code):
parser = CodeParser()
tree = ast.parse(source_code)
parser.visit(tree)
return {
“classes”: parser.classes,
“functions”: parser.functions,
“imports”: parser.imports
}

Data Storage

Once the code is parsed, the next step is to store the extracted information in a structured format. Using a simple JSON format can be effective for this purpose.

python

import json

def store_data(parsed_data, file_path):
with open(file_path, ‘w’) as f:
json.dump(parsed_data, f, indent=4)

# Example usage:
source_code = “””
import os

class ExampleClass:
def example_method(self):
pass

def example_function():
pass
“””

parsed_data = parse_code(source_code)
store_data(parsed_data, ‘parsed_code.json’)

Diagram Generation

Generating diagrams from the stored data involves converting the JSON data into a visual format. We can use a library like Graphviz to create architecture diagrams.

python

from graphviz import Digraph

def generate_diagram(data, output_file):
dot = Digraph()

# Add classes
for cls in data[“classes”]:
dot.node(cls, shape=“box”)

# Add functions
for func in data[“functions”]:
dot.node(func, shape=“ellipse”)

# Add relationships (for simplicity, we only show imports here)
for imp in data[“imports”]:
dot.edge(imp, data[“classes”][0] if data[“classes”] else imp)

dot.render(output_file)

# Example usage:
generate_diagram(parsed_data, ‘architecture_diagram’)

User Interface

A user-friendly interface is essential for interacting with the diagrams. This can be achieved using a web-based interface with Flask and D3.js for dynamic visualization.

python

from flask import Flask, render_template, send_file

import json

app = Flask(__name__)@app.route(‘/’)
def index():
return render_template(‘index.html’)@app.route(‘/data’)
def data():
with open(‘parsed_code.json’) as f:
data = json.load(f)
return data@app.route(‘/diagram’)
def diagram():
return send_file(‘architecture_diagram.gv.svg’)if __name__ == ‘__main__’:
app.run(debug=True)

In the templates directory, create an index.html file for the frontend.

html

<!DOCTYPE html>

<html>

<head>

<title>Architecture Diagram</title>

<script src="https://d3js.org/d3.v7.min.js"></script>

</head>

<body>

<h1>Architecture Diagram</h1>

<div id="diagram"></div>

Conclusion

Building a tool to map codebases from the source and automate the creation of application architecture diagrams is a powerful way to enhance software development practices. This process involves several steps: parsing the source code, storing the extracted data, generating visual diagrams, and providing a user-friendly interface for interaction.

By automating architecture diagrams, developers can ensure that the visual representations of their codebases are always accurate and up-to-date, significantly reducing the risk of miscommunication and improving overall project efficiency. The example provided uses Python for code parsing, JSON for data storage, Graphviz for diagram generation, and Flask with D3.js for the user interface, offering a comprehensive approach to achieving this automation.

Future enhancements to this tool could include support for multiple programming languages, more sophisticated relationship detection, and advanced visualization techniques to provide even deeper insights into the architecture and dependencies of complex codebases. With continuous improvements, such tools can become invaluable assets in the toolkit of modern software developers.