# PyBugHunt
## Advanced Python Code Error Detection and Analysis
PyBugHunt is a sophisticated Python library designed to detect, analyze, and suggest fixes for both syntactical and logical errors in Python code. Leveraging a combination of static code analysis techniques and advanced transformer-based machine learning models, PyBugHunt offers developers a powerful tool to improve code quality and reduce debugging time.
---
## Table of Contents
- [Features](#features)
- [Technology Stack](#technology-stack)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Command Line Interface](#command-line-interface)
- [Python API](#python-api)
- [Error Detection Capabilities](#error-detection-capabilities)
- [Machine Learning Approach](#machine-learning-approach)
- [Development](#development)
- [License](#license)
- [Contributing](#contributing)
---
## Features
PyBugHunt offers comprehensive error detection capabilities:
- **Robust Syntax Error Detection and Analysis**
- **Intelligent Logical Error Detection** using both static analysis and machine learning.
- **Transformer-Based Models**:
- **CodeBERT**: For classifying code as correct or containing a logical error.
- **T5 (Text-to-Text Transfer Transformer)**: For generating natural language descriptions of the detected errors.
- **Fix Suggestion System**
- **Flexible Integration Options** (CLI and Python API)
- **Customization and Training** of models.
---
## Technology Stack
PyBugHunt utilizes a wide range of technologies and libraries:
### Core Technologies
- **Python 3.8+**
- **Abstract Syntax Tree (AST)**
- **Python Standard Library**
### Machine Learning
- **PyTorch**
- **Hugging Face Transformers** (for CodeBERT and T5)
- **scikit-learn**
- **NumPy**
### Static Analysis
- **Astroid**
- **PyLint**
---
## Project Structure
```
pybughunt/
├── src/
│ └── pybughunt/
│ ├── __init__.py
│ ├── cli.py
│ ├── detector.py
│ ├── logic_analyzer.py
│ ├── syntax_analyzer.py
│ └── models/
│ ├── __init__.py
│ ├── model_loader.py
│ ├── model_trainer.py
│ └── models.py # New file for transformer model definitions
├── tests/
├── .gitignore
├── README.md
├── pyproject.toml
└── setup.py
```
---
## Installation
### From Source
```bash
# Clone the repository
git clone https://github.com/Preksha-7/pybughunt.git
cd pybughunt
# Install in development mode
pip install -e .
```
### Dependencies
All dependencies will be automatically installed. The main dependencies are listed in `pyproject.toml` and `setup.py`.
---
## Usage
### Command Line Interface
Analyze a file with the default static analysis:
```bash
python -m pybughunt.cli analyze src/pybughunt/sample_buggy.py --model_type static
```
Analyze a file using a specific machine learning model:
```bash
python -m pybughunt.cli analyze src/pybughunt/sample_buggy.py --model_type codebert --model_path path/to/saved_codebert_model
```
Train a new model:
```bash
python -m pybughunt.cli train --dataset /path/to/python/files --output my_model --model_type codebert
```
### Python API
```python
from pybughunt import CodeErrorDetector
# Initialize the detector with a specific model
detector = CodeErrorDetector(model_type='codebert', model_path='path/to/saved_codebert_model')
code = '''
def incorrect_factorial(n):
if n == 0:
return 1
else:
return incorrect_factorial(n-1) # Missing multiplication with n
'''
results = detector.analyze(code)
print(results)
```
---
## Error Detection Capabilities
**Syntax Errors:** Missing delimiters, indentation issues, invalid syntax, etc.
**Logical Errors:**
- **Static Analysis:** Infinite loops, unused variables, off-by-one errors, division by zero, unreachable code.
- **Machine Learning:** More subtle logical errors detected by the trained transformer models.
---
## Machine Learning Approach
PyBugHunt now includes transformer-based models for more advanced logical error detection:
**CodeBERT (microsoft/codebert-base):** A model pre-trained on a large corpus of code, used for classifying code snippets as either correct or containing a logical error.
**T5 (t5-small):** A sequence-to-sequence model that can be trained to generate a natural language description of the error in a piece of code.
These models can be trained on your own dataset using the `train` command in the CLI.
---
## Development
The project is structured to be modular and extensible. You can add new error detection patterns to the `logic_analyzer.py` or experiment with different models in the `models/` directory.
---
## License
This project is licensed under the MIT License. See the LICENSE file for details.
---
## Contributing
Contributions are welcome! Please feel free to submit a pull request.
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"description": "# PyBugHunt\r\n\r\n## Advanced Python Code Error Detection and Analysis\r\n\r\nPyBugHunt is a sophisticated Python library designed to detect, analyze, and suggest fixes for both syntactical and logical errors in Python code. Leveraging a combination of static code analysis techniques and advanced transformer-based machine learning models, PyBugHunt offers developers a powerful tool to improve code quality and reduce debugging time.\r\n\r\n\r\n\r\n\r\n\r\n---\r\n\r\n## Table of Contents\r\n\r\n- [Features](#features)\r\n- [Technology Stack](#technology-stack)\r\n- [Project Structure](#project-structure)\r\n- [Installation](#installation)\r\n- [Usage](#usage)\r\n - [Command Line Interface](#command-line-interface)\r\n - [Python API](#python-api)\r\n- [Error Detection Capabilities](#error-detection-capabilities)\r\n- [Machine Learning Approach](#machine-learning-approach)\r\n- [Development](#development)\r\n- [License](#license)\r\n- [Contributing](#contributing)\r\n\r\n---\r\n\r\n## Features\r\n\r\nPyBugHunt offers comprehensive error detection capabilities:\r\n\r\n- **Robust Syntax Error Detection and Analysis**\r\n- **Intelligent Logical Error Detection** using both static analysis and machine learning.\r\n- **Transformer-Based Models**:\r\n - **CodeBERT**: For classifying code as correct or containing a logical error.\r\n - **T5 (Text-to-Text Transfer Transformer)**: For generating natural language descriptions of the detected errors.\r\n- **Fix Suggestion System**\r\n- **Flexible Integration Options** (CLI and Python API)\r\n- **Customization and Training** of models.\r\n\r\n---\r\n\r\n## Technology Stack\r\n\r\nPyBugHunt utilizes a wide range of technologies and libraries:\r\n\r\n### Core Technologies\r\n\r\n- **Python 3.8+**\r\n- **Abstract Syntax Tree (AST)**\r\n- **Python Standard Library**\r\n\r\n### Machine Learning\r\n\r\n- **PyTorch**\r\n- **Hugging Face Transformers** (for CodeBERT and T5)\r\n- **scikit-learn**\r\n- **NumPy**\r\n\r\n### Static Analysis\r\n\r\n- **Astroid**\r\n- **PyLint**\r\n\r\n---\r\n\r\n## Project Structure\r\n\r\n```\r\npybughunt/\r\n\u251c\u2500\u2500 src/\r\n\u2502 \u2514\u2500\u2500 pybughunt/\r\n\u2502 \u251c\u2500\u2500 __init__.py\r\n\u2502 \u251c\u2500\u2500 cli.py\r\n\u2502 \u251c\u2500\u2500 detector.py\r\n\u2502 \u251c\u2500\u2500 logic_analyzer.py\r\n\u2502 \u251c\u2500\u2500 syntax_analyzer.py\r\n\u2502 \u2514\u2500\u2500 models/\r\n\u2502 \u251c\u2500\u2500 __init__.py\r\n\u2502 \u251c\u2500\u2500 model_loader.py\r\n\u2502 \u251c\u2500\u2500 model_trainer.py\r\n\u2502 \u2514\u2500\u2500 models.py # New file for transformer model definitions\r\n\u251c\u2500\u2500 tests/\r\n\u251c\u2500\u2500 .gitignore\r\n\u251c\u2500\u2500 README.md\r\n\u251c\u2500\u2500 pyproject.toml\r\n\u2514\u2500\u2500 setup.py\r\n```\r\n\r\n---\r\n\r\n## Installation\r\n\r\n### From Source\r\n\r\n```bash\r\n# Clone the repository\r\ngit clone https://github.com/Preksha-7/pybughunt.git\r\ncd pybughunt\r\n\r\n# Install in development mode\r\npip install -e .\r\n```\r\n\r\n### Dependencies\r\n\r\nAll dependencies will be automatically installed. The main dependencies are listed in `pyproject.toml` and `setup.py`.\r\n\r\n---\r\n\r\n## Usage\r\n\r\n### Command Line Interface\r\n\r\nAnalyze a file with the default static analysis:\r\n\r\n```bash\r\npython -m pybughunt.cli analyze src/pybughunt/sample_buggy.py --model_type static\r\n```\r\n\r\nAnalyze a file using a specific machine learning model:\r\n\r\n```bash\r\npython -m pybughunt.cli analyze src/pybughunt/sample_buggy.py --model_type codebert --model_path path/to/saved_codebert_model\r\n```\r\n\r\nTrain a new model:\r\n\r\n```bash\r\npython -m pybughunt.cli train --dataset /path/to/python/files --output my_model --model_type codebert\r\n```\r\n\r\n### Python API\r\n\r\n```python\r\nfrom pybughunt import CodeErrorDetector\r\n\r\n# Initialize the detector with a specific model\r\ndetector = CodeErrorDetector(model_type='codebert', model_path='path/to/saved_codebert_model')\r\n\r\ncode = '''\r\ndef incorrect_factorial(n):\r\n if n == 0:\r\n return 1\r\n else:\r\n return incorrect_factorial(n-1) # Missing multiplication with n\r\n'''\r\n\r\nresults = detector.analyze(code)\r\nprint(results)\r\n```\r\n\r\n---\r\n\r\n## Error Detection Capabilities\r\n\r\n**Syntax Errors:** Missing delimiters, indentation issues, invalid syntax, etc.\r\n\r\n**Logical Errors:**\r\n\r\n- **Static Analysis:** Infinite loops, unused variables, off-by-one errors, division by zero, unreachable code.\r\n- **Machine Learning:** More subtle logical errors detected by the trained transformer models.\r\n\r\n---\r\n\r\n## Machine Learning Approach\r\n\r\nPyBugHunt now includes transformer-based models for more advanced logical error detection:\r\n\r\n**CodeBERT (microsoft/codebert-base):** A model pre-trained on a large corpus of code, used for classifying code snippets as either correct or containing a logical error.\r\n\r\n**T5 (t5-small):** A sequence-to-sequence model that can be trained to generate a natural language description of the error in a piece of code.\r\n\r\nThese models can be trained on your own dataset using the `train` command in the CLI.\r\n\r\n---\r\n\r\n## Development\r\n\r\nThe project is structured to be modular and extensible. You can add new error detection patterns to the `logic_analyzer.py` or experiment with different models in the `models/` directory.\r\n\r\n---\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License. See the LICENSE file for details.\r\n\r\n---\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a pull request.\r\n",
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