Example Weaknesses Overview

Example Weaknesses Overview#

This notebook demonstrates how to use the Python Code Audit APIs to analyze and visualize security weaknesses detected in a codebase.

We focus on two key visualizations:

  • Weakness distribution across the entire project

  • Per-file issue overview to identify high-risk files

These plots help prioritize security reviews and refactoring efforts.

Import APIs#

We import two visualization functions and the scan interface:

  • filescan → performs a SAST scan on a package

  • weaknesses_overview → aggregates common weaknesses

  • sast_files_overview → shows issues per file

These APIs are designed to work together: Scan → Aggregate → Visualize

from codeaudit.altairplots import weaknesses_overview, sast_files_overview # Generates an interactive visualization from scan results

from codeaudit.api_interfaces import filescan #Used to perform a scan

Enable Interactive Rendering#

The visualizations use Altair + Panel for interactivity.

Panel is required to properly render charts inside notebooks. Panel is currently NOT installed when installing Python Code Audit, so install it with:

pip install panel

#To display Panel UI things in a notebook, the following lines are needed
import panel as pn

pn.extension()

Choose a Target Package#

Specify the Python package you want to analyze.

This can be:

  • A package from PyPI

  • A local project

Example below uses a small public package for demonstration.

packagename = "smolagents" #You can use any Python package present on PyPI.org, or a local package.

Run Security Scan#

The filescan API analyzes the package and extracts:

  • File-level metadata

  • Complexity metrics

  • Detected security weaknesses (SAST findings)

The result is a structured dictionary used by visualization APIs.

scanresult = filescan(packagename)

Visualize Common Weaknesses#

The weaknesses_overview plot shows the most frequent security patterns across the entire codebase.

What this shows:#

  • Common risky constructs (e.g. exec, subprocess, assert)

  • Frequency of each issue

  • Top findings highlighted for quick inspection

Why it matters:#

This helps identify systematic security risks and recurring anti-patterns that should be addressed globally.

weaknesses_overview(scanresult)

Interpretation Guide#

  • Higher bars → more frequent usage → higher audit priority

  • Top 5 highlighted → most critical patterns to review first

  • Use this plot to:

    • Detect insecure coding habits

    • Guide secure coding training

    • Prioritize remediation efforts

Visualize Issues Per File#

The sast_files_overview plot shows how security issues are distributed across individual files.

What this shows:#

  • Number of issues per file

  • Relative risk concentration

  • File complexity (via tooltip)

Why it matters:#

This helps identify high-risk files that require immediate attention.

sast_files_overview(scanresult)

Interpretation Guide#

  • Files with more issues → higher audit priority

  • Combine with complexity to identify:

    • Hard-to-maintain risky files

    • Refactoring candidates

Use Cases#

These visualizations are useful for:

  • 🔍 Security audits → Quickly identify risk hotspots

  • 🧹 Refactoring → Target complex + vulnerable files

  • 👥 Code reviews → Focus reviewer effort

  • 🚀 CI/CD → Track security trends over time

In this notebook is shown:

  • How to run a SAST scan using filescan

  • Aggregate weaknesses across a codebase

  • Visualize security risks using interactive plots

  • Identify high-risk files and patterns

Hint

The Python Code Audit APIs enable fast, visual security analysis directly in Python security workflows.