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

1

Install Package

First, install the PraisonAI Agents package:

pip install praisonaiagents gitingest
2

Set API Key

Set your OpenAI API key as an environment variable in your terminal:

export OPENAI_API_KEY=your_api_key_here
3

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

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