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Advanced File Type Recognition

Our platform automatically categorizes and adjusts metrics based on deep analysis of file types, patterns, and development contexts. This ensures that productivity measurements accurately reflect human engineering contributions rather than automated or generated content.

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Machine Learning Artifacts & Notebooks

Jupyter Notebooks (.ipynb): Our algorithm recognizes that data science notebooks often contain significant amounts of auto-generated output cells, execution metadata, and visualization artifacts. The system intelligently filters these elements to focus on the actual analytical code and markdown documentation written by engineers.

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Dependency & Build Management

Package Lock Files: Our system automatically identifies and appropriately weights dependency lock files (package-lock.json, yarn.lock, Pipfile.lock, poetry.lock) which are entirely machine-generated. This prevents artificial inflation of productivity metrics from automated dependency resolution. Build Artifacts: Minified JavaScript, CSS bundles, and compiled assets are intelligently recognized and adjusted to ensure metrics reflect source code authorship rather than build tool output.

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Generated Code Detection

API Clients & SDKs: The platform employs pattern recognition to identify auto-generated API clients, protocol buffer implementations, and SDK code. Files in /generated/ directories, .pb.go, .pb.py, and similar patterns are automatically detected and appropriately weighted. Benefits for Modern Development:
  • Accurate metrics in microservices architectures
  • Fair assessment in API-first development workflows
  • Proper attribution in polyglot environments

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Database & Infrastructure Code

Migration Files: Our algorithm recognizes database migration patterns and adjusts for the fact that many migrations are generated by ORM tools or database frameworks, while still crediting the engineering effort involved in schema design and data transformation logic. Configuration Management: The system intelligently analyzes YAML, JSON, and TOML configuration files, distinguishing between hand-crafted configuration and auto-generated settings files.

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Documentation Intelligence

Generated Documentation: Our platform recognizes auto-generated README files, API documentation, and changelog entries while still properly crediting manually authored documentation that represents genuine knowledge work.

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Precision & Accuracy

This intelligent analysis ensures that Bilanc’s metrics provide:
  • True Productivity Measurement: Focus on human engineering effort rather than tool output
  • Fair Team Comparisons: Consistent measurement across different technology stacks and development practices
  • Strategic Insights: Accurate data for engineering leadership decision-making
  • Developer Recognition: Proper attribution of complex, high-value engineering work

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Continuous Learning

Our algorithms continuously evolve to recognize new development patterns, frameworks, and tools, ensuring that your metrics remain accurate as your technology stack and development practices evolve.
This intelligent analysis runs automatically on all code contributions, requiring no configuration or manual intervention from your team.