codeanalyzer-python 0.1.13

Static Analysis on Python source code using Jedi, CodeQL and Treesitter.

A Python Static Analysis Toolkit (and Library)

A comprehensive static analysis tool for Python source code that provides symbol table generation, call graph analysis, and semantic analysis using Jedi, CodeQL, and Tree-sitter.

Installation

pip install codeanalyzer-python

Prerequisites

• Python 3.12 or higher

System Package Requirements

The tool creates virtual environments internally using Python's built-in venv module.

Ubuntu/Debian systems:

sudo apt update
sudo apt install python3.12-venv python3-dev build-essential

Fedora/RHEL/CentOS systems:

sudo dnf group install "Development Tools"
sudo dnf install python3-pip python3-venv python3-devel

or on older versions:

sudo yum groupinstall "Development Tools"
sudo yum install python3-pip python3-venv python3-devel

macOS systems:

# Install Xcode Command Line Tools (for compilation)
xcode-select --install

# If using Homebrew Python (recommended)
brew install python@3.12

# If using pyenv (popular Python version manager)
# First ensure pyenv is properly installed and configured
pyenv install 3.12.0  # or latest 3.12.x version
pyenv global 3.12.0   # or pyenv local 3.12.0 for project-specific

# If using system Python, you may need to install certificates
/Applications/Python\ 3.12/Install\ Certificates.command

Note: These packages are required as the tool uses Python's built-in venv module to create isolated environments for analysis.

Usage

The codeanalyzer provides a command-line interface for performing static analysis on Python projects.

Basic Usage

codeanalyzer --input /path/to/python/project

Command Line Options

To view the available options and commands, run codeanalyzer --help. You should see output similar to the following:

❯ codeanalyzer --help

 Usage: codeanalyzer [OPTIONS] COMMAND [ARGS]...

 Static Analysis on Python source code using Jedi, CodeQL and Tree sitter.


╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *  --input           -i                  PATH            Path to the project root directory. [default: None] [required]   │
│    --output          -o                  PATH            Output directory for artifacts. [default: None]                  │
│    --format          -f                  [json|msgpack]  Output format: json or msgpack. [default: json]                  │
│    --analysis-level  -a                  INTEGER         1: symbol table, 2: call graph. [default: 1]                     │
│    --codeql              --no-codeql                     Enable CodeQL-based analysis. [default: no-codeql]               │
│    --eager               --lazy                          Enable eager or lazy analysis. Defaults to lazy. [default: lazy] │
│    --cache-dir       -c                  PATH            Directory to store analysis cache. [default: None]               │
│    --clear-cache         --keep-cache                    Clear cache after analysis. [default: clear-cache]               │
│                      -v                  INTEGER         Increase verbosity: -v, -vv, -vvv [default: 0]                   │
│    --help                                                Show this message and exit.                                      │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Examples

1. Basic analysis with symbol table:

   codeanalyzer --input ./my-python-project
   

   This will print the symbol table to stdout in JSON format to the standard output. If you want to save the output, you can use the --output option.

   codeanalyzer --input ./my-python-project --output /path/to/analysis-results
   

   Now, you can find the analysis results in analysis.json in the specified directory.

2. Change output format to msgpack:

   codeanalyzer --input ./my-python-project --output /path/to/analysis-results --format msgpack
   

   This will save the analysis results in analysis.msgpack in the specified directory.

3. Toggle analysis levels with --analysis-level:

   codeanalyzer --input ./my-python-project --analysis-level 1 # Symbol table only
   

   Call graph analysis can be enabled by setting the level to 2:

   codeanalyzer --input ./my-python-project --analysis-level 2 # Symbol table + Call graph
   

   Note: The --analysis-level=2 is not yet implemented in this version.

4. Analysis with CodeQL enabled:

   codeanalyzer --input ./my-python-project --codeql
   

   This will perform CodeQL-based analysis in addition to the standard symbol table generation.

   Note: Not yet fully implemented. Please refrain from using this option until further notice.

5. Eager analysis with custom cache directory:

   codeanalyzer --input ./my-python-project --eager --cache-dir /path/to/custom-cache
   

   This will rebuild the analysis cache at every run and store it in /path/to/custom-cache/.codeanalyzer. The cache will be cleared by default after analysis unless you specify --keep-cache.

   If you provide --cache-dir, the cache will be stored in that directory. If not specified, it defaults to .codeanalyzer in the current working directory ($PWD).

6. Quiet mode (minimal output):

   codeanalyzer --input /path/to/my-python-project --quiet
   

Output

By default, analysis results are printed to stdout in JSON format. When using the --output option, results are saved to analysis.json in the specified directory. If you use the --format=msgpack option, the results will be saved in analysis.msgpack, which is a binary format that can be more efficient for storage and transmission.

Development

This project uses uv for dependency management during development.

Development Setup

1. Install uv

A Python Static Analysis Toolkit (and Library)

A comprehensive static analysis tool for Python source code that provides symbol table generation, call graph analysis, and semantic analysis using Jedi, CodeQL, and Tree-sitter.

Installation

pip install codeanalyzer-python

Prerequisites

• Python 3.12 or higher

System Package Requirements

The tool creates virtual environments internally using Python's built-in venv module.

Ubuntu/Debian systems:

sudo apt update
sudo apt install python3.12-venv python3-dev build-essential

Fedora/RHEL/CentOS systems:

sudo dnf group install "Development Tools"
sudo dnf install python3-pip python3-venv python3-devel

or on older versions:

sudo yum groupinstall "Development Tools"
sudo yum install python3-pip python3-venv python3-devel

macOS systems:

# Install Xcode Command Line Tools (for compilation)
xcode-select --install

# If using Homebrew Python (recommended)
brew install python@3.12

# If using pyenv (popular Python version manager)
# First ensure pyenv is properly installed and configured
pyenv install 3.12.0  # or latest 3.12.x version
pyenv global 3.12.0   # or pyenv local 3.12.0 for project-specific

# If using system Python, you may need to install certificates
/Applications/Python\ 3.12/Install\ Certificates.command

Note: These packages are required as the tool uses Python's built-in venv module to create isolated environments for analysis.

Usage

The codeanalyzer provides a command-line interface for performing static analysis on Python projects.

Basic Usage

codeanalyzer --input /path/to/python/project

Command Line Options

To view the available options and commands, run codeanalyzer --help. You should see output similar to the following:

❯ codeanalyzer --help

 Usage: codeanalyzer [OPTIONS] COMMAND [ARGS]...

 Static Analysis on Python source code using Jedi, CodeQL and Tree sitter.


╭─ Options ──────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ *  --input           -i                  PATH     Path to the project root directory. [default: None] [required]   │
│    --output          -o                  PATH     Output directory for artifacts. [default: None]                  │
│    --format          -f           [json|msgpack]  Output format: json or msgpack. [default: json].                 │
│    --analysis-level  -a                  INTEGER  1: symbol table, 2: call graph. [default: 1]                     │
│    --codeql              --no-codeql              Enable CodeQL-based analysis. [default: no-codeql]               │
│    --eager               --lazy                   Enable eager or lazy analysis. Defaults to lazy. [default: lazy] │
│    --cache-dir       -c                  PATH     Directory to store analysis cache. [default: None]               │
│    --clear-cache         --keep-cache             Clear cache after analysis. [default: clear-cache]               │
│                      -v                  INTEGER  Increase verbosity: -v, -vv, -vvv [default: 0]                   │
│    --help                                         Show this message and exit.                                      │
╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

Examples

1. Basic analysis with symbol table:

   codeanalyzer --input ./my-python-project
   

   This will print the symbol table to stdout in JSON format to the standard output. If you want to save the output, you can use the --output option.

   codeanalyzer --input ./my-python-project --output /path/to/analysis-results
   

   Now, you can find the analysis results in analysis.json in the specified directory.

2. Toggle analysis levels with --analysis-level:

   codeanalyzer --input ./my-python-project --analysis-level 1 # Symbol table only
   

   Call graph analysis can be enabled by setting the level to 2:

   codeanalyzer --input ./my-python-project --analysis-level 2 # Symbol table + Call graph
   

   Note: The --analysis-level=2 is not yet implemented in this version.

3. Analysis with CodeQL enabled:

   codeanalyzer --input ./my-python-project --codeql
   

   This will perform CodeQL-based analysis in addition to the standard symbol table generation.

   Note: Not yet fully implemented. Please refrain from using this option until further notice.

4. Eager analysis with custom cache directory:

   codeanalyzer --input ./my-python-project --eager --cache-dir /path/to/custom-cache
   

   This will rebuild the analysis cache at every run and store it in /path/to/custom-cache/.codeanalyzer. The cache will be cleared by default after analysis unless you specify --keep-cache.

   If you provide --cache-dir, the cache will be stored in that directory. If not specified, it defaults to .codeanalyzer in the current working directory ($PWD).

5. Save output in msgpack format:

   codeanalyzer --input ./my-python-project --output /path/to/analysis-results --format msgpack
   

Output

By default, analysis results are printed to stdout in JSON format. When using the --output option, results are saved to analysis.json in the specified directory.

Development

This project uses uv for dependency management during development.

Development Setup

1. Install uv

2. Clone the repository:

   git clone https://github.com/codellm-devkit/codeanalyzer-python
   cd codeanalyzer-python
   

3. Install dependencies using uv:

   uv sync --all-groups
   

   This will install all dependencies including development and test dependencies.

Running from Source

When developing, you can run the tool directly from source:

uv run codeanalyzer --input /path/to/python/project

Running Tests

uv run pytest --pspec -s

Development Dependencies

The project includes additional dependency groups for development:

• test: pytest and related testing tools
• dev: development tools like ipdb

Install all groups with:

uv sync --all-groups

2. Clone the repository:

   git clone https://github.com/codellm-devkit/codeanalyzer-python
   cd codeanalyzer-python
   

3. Install dependencies using uv:

   uv sync --all-groups
   

   This will install all dependencies including development and test dependencies.

Running from Source

When developing, you can run the tool directly from source:

uv run codeanalyzer --input /path/to/python/project

Running Tests

uv run pytest --pspec -s

Development Dependencies

The project includes additional dependency groups for development:

• test: pytest and related testing tools
• dev: development tools like ipdb

Install all groups with:

uv sync --all-groups