This script analyzes a codebase to measure and report the impact of AI-generated code contributions. It provides detailed insights about AI vs human contributions, helping teams understand the role of AI in their development process.

• Repository Analysis: Automatically detects and analyzes git repositories:

  • Uses current directory if it's a git repo

  • Searches parent directories for a git repo

  • Falls back to cloning a specified repository if needed

  # Basic repository setup
  repo_path = os.getcwd()
  repo_config = RepoConfig.from_repo_path(repo_path)
  repo_operator = RepoOperator(repo_config=repo_config)
  project = ProjectConfig.from_repo_operator(repo_operator=repo_operator, programming_language=ProgrammingLanguage.PYTHON)
  codebase = Codebase(projects=[project])

• Comprehensive Statistics:

  • Total number of commits and AI vs human contribution percentages
  • Files with significant AI contribution (>50%)
  • AI-touched symbols and their impact
  • Detailed contributor breakdown (human and AI contributors)

  # Run the analysis
  ai_authors = ["github-actions[bot]", "dependabot[bot]"]
  results = analyze_ai_impact(codebase, ai_authors)
  
  # Access statistics
  stats = results["stats"]
  print(f"Total commits: {stats['total_commits']}")
  print(f"AI commits: {stats['ai_commits']} ({stats['ai_percentage']:.1f}%)")
  print(f"Files with >50% AI: {stats['ai_file_count']} of {stats['total_file_count']}")
  
  # View contributors
  for author, count in results["contributors"]:
      is_ai = any(ai_name in author for ai_name in ai_authors)
      print(f"{'🤖' if is_ai else '👤'} {author}: {count} commits")

• High-Impact Code Detection:

  • Identifies AI-written code that is heavily used by other parts of the codebase
  • Shows dependency relationships for AI-contributed code

  # Access high-impact AI symbols
  for symbol in results["high_impact_symbols"]:
      print(f"Symbol: {symbol['name']} ({symbol['filepath']})")
      print(f"Used by {symbol['usage_count']} other symbols")
      print(f"Last edited by: {symbol['last_editor']}")
  
  # View top AI-contributed files
  for file_path, percentage in stats["top_ai_files"]:
      print(f"{file_path}: {percentage:.1f}% AI contribution")

• Detailed Attribution:

  • Maps symbols to git history
  • Tracks last editor and complete editor history for each symbol
  • Flags AI-authored symbols

  # Get attribution information for a specific symbol
  symbol = codebase.get_symbol("path/to/file.py:MyClass.my_method")
  
  # Access attribution data
  print(f"Last editor: {symbol.last_editor}")
  print(f"Editor history: {symbol.editor_history}")
  print(f"AI authored: {symbol.is_ai_authored}")
  
  # Find all AI-authored symbols
  ai_symbols = [s for s in codebase.get_symbols() if s.is_ai_authored]
  for symbol in ai_symbols:
      print(f"AI symbol: {symbol.name}")

The script generates:

1. Console output with summary statistics
2. Detailed analysis in ai_impact_analysis.json
3. Attribution information added to codebase symbols

python run.py

The script will automatically:

1. Initialize and analyze the codebase
2. Process git history
3. Generate attribution information
4. Output detailed statistics

You can also visualize the AI impact analysis results using a dashboard. For setup and usage instructions, please see the documentation in the /dashboard subdirectory.

After running the analysis, symbols in the codebase will have the following attribution information:

• symbol.last_editor: The last person who edited the symbol
• symbol.editor_history: List of all editors who have touched the symbol
• symbol.is_ai_authored: Boolean indicating if the symbol was authored by AI

• Attributions
• Codegen Documentation

Feel free to submit issues and enhancement requests!