What is Code Analysis?
Code Analysis is a systematic process of evaluating source code to assess its quality, maintainability, performance, and security. This helps developers and organizations maintain high code standards and identify areas for improvement.
Features
Quality Assessment Comprehensive evaluation of code quality with numerical scoring.
Core Metrics Analysis Analysis of architecture, maintainability, performance, and security.
Technical Assessment Review of tech stack, complexity, and best practices adherence.
Risk Assessment Identification of potential risks and security vulnerabilities.
Recommendations Actionable suggestions for improvements and enhancements.
Quick Start
Install Package
First, install the PraisonAI Agents package:
pip install praisonaiagents gitingest
Set API Key
Set your OpenAI API key as an environment variable in your terminal:
export OPENAI_API_KEY = your_api_key_here
Create a file
Create a new file code_analysis.py
with the following code:
from praisonaiagents import Agent, Task, PraisonAIAgents
from pydantic import BaseModel
from typing import List, Dict
from gitingest import ingest
class CodeMetrics ( BaseModel ):
category: str
score: int
findings: List[ str ]
class CodeAnalysisReport ( BaseModel ):
overall_quality: int
code_metrics: List[CodeMetrics]
architecture_score: int
maintainability_score: int
performance_score: int
security_score: int
test_coverage: int
key_strengths: List[ str ]
improvement_areas: List[ str ]
tech_stack: List[ str ]
recommendations: List[ str ]
complexity_metrics: Dict[ str , int ]
best_practices: List[Dict[ str , str ]]
potential_risks: List[ str ]
documentation_quality: int
code_analyzer = Agent(
role = "Code Analysis Expert" ,
goal = "Provide comprehensive code evaluation and recommendations" ,
backstory = """Expert code analyst specializing in architecture review,
best practices, and technical debt assessment.""" ,
verbose = True
)
code_analysis_task = Task(
description = """Analyze code repository and provide structured evaluation:
1. Overall Quality (0-100)
2. Core Metrics Analysis:
- Architecture and Design
- Code Maintainability
- Performance Optimization
- Security Practices
- Test Coverage
3. Technical Assessment:
- Technology Stack Review
- Code Complexity Analysis
- Best Practices Adherence
- Risk Assessment
4. Recommendations:
- Key Improvements
- Architecture Suggestions
- Security Enhancements""" ,
expected_output = "Detailed code analysis report with metrics and recommendations" ,
agent = code_analyzer,
output_pydantic = CodeAnalysisReport
)
def analyze_code ( code_source : str ) -> CodeAnalysisReport:
"""
Analyze code from directory path or GitHub URL
"""
# Ingest code content
summary, tree, content = ingest(code_source)
# Concatenate context into structured format
context_text = f """
CODE REPOSITORY ANALYSIS
=======================
SUMMARY
-------
{ summary }
REPOSITORY STRUCTURE
-------------------
{ tree }
SOURCE CODE
-----------
{ content }
"""
# Initialize and run analysis
agents = PraisonAIAgents(
agents = [code_analyzer],
tasks = [code_analysis_task]
)
return agents.start(context_text)
if __name__ == "__main__" :
# Example usage
code_source = "https://github.com/openai/openai-python/tree/main/src/openai/cli/_api/chat" # GitHub URL or local directory
result = analyze_code(code_source)
print (result)
Understanding the Output
The code analysis generates a comprehensive report with the following components:
Overall Quality Score (0-100)
Core Metrics Analysis
Architecture and Design Score
Code Maintainability Score
Performance Score
Security Score
Test Coverage Percentage
Technical Assessment
Technology Stack
Complexity Metrics
Best Practices Review
Risk Assessment
Recommendations
Key Improvements
Architecture Suggestions
Security Enhancements
Next Steps