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Overview of Methodology & Risk Exposure Index

The Core Philosophy: From Raw Text to Relational Knowledge Graph

GitGalaxy does not measure subjective "Code Quality," which implies judgment. Instead, it measures objective Risk Exposures.

By utilizing deterministic keyword heuristics, the engine parses raw text to build a massive, interconnected knowledge graph of the entire repository. We extract structural markers from the code (the data) and translate them into visible risk heatmaps (the projection). This allows architecture teams to instantly see where their system is drifting into dangerous territory without reading a single line of code.

The Structural Taxonomy of GitGalaxy

GitGalaxy assesses functions against 50+ metrics. We then roll these measurements up to the class, file, folder and repo level.

The Universal Risk Spectrum

Regardless of the risk metric being viewed, the visual translation is always the same color palette: * 🟦 Blue: Very Low Exposure * 🩵 Cyan: Low Exposure * 🟨 Yellow: Moderate / Intermediate Exposure * 🟧 Orange: High Exposure * 🟥 Bright Red: Critical / Extreme Exposure (Hot / Dangerous)

Primary Risk Exposures

Definition: The mathematical output of parsing regex heuristics, file boundaries, version control counts, and ecosystem multipliers. These metrics represent the unified risk calculations applied across the repository.

Metric Level 1: Function
(count based)
(0-infinity)		 Level 2: Class
(count based)
(0-infinity)		 Level 3: File
(Sigmoid normalized)
(0-100)		 Level 4: Folder
(Weighted Avg)
(0-100)		 Level 5: Repo
(Weighted Avg)
(0-100)
Cognitive Load # of branches, mutations, and danger triggers, adjusted Sum of function counts + Gini Normalized via sigmoid function + GuideStar Shield Mass-Weighted Avg Mass-Weighted Avg
State Flux # of variable mutations, adjusted Sum of function counts + LCOM Normalized via sigmoid function Mass-Weighted Avg Mass-Weighted Avg
Concurrency # of async/thread operations minus locks, adjusted Sum of function counts Normalized via sigmoid function Mass-Weighted Avg Mass-Weighted Avg
Technical Debt # of engineer comments (ex: HACK, TODO), adjusted Sum of function counts Normalized via sigmoid function Mass-Weighted Avg Mass-Weighted Avg
Structural Fortification # of attacker vs. defender keywords, adjusted Sum of function counts Normalized via sigmoid function w/ Breach Floor Mass-Weighted Avg Mass-Weighted Avg
Graveyard N/A N/A Commented out code lines, normalized via sigmoid function Mass-Weighted Avg Mass-Weighted Avg
Specification Alignment # of functions with Spec tags, adjusted Sum of function counts w/ Spec mapping Percentage of entities lacking spec tags, adjusted Mass-Weighted Avg Mass-Weighted Avg
Civil War (Tabs/Spaces) N/A N/A Ratio of tab vs space indented lines, percentage Mass-Weighted Avg Mass-Weighted Avg
Ownership Entropy N/A N/A Distribution of git authors, normalized via Shannon entropy Mass-Weighted Avg Mass-Weighted Avg
Deep Churn N/A N/A Total git commits over time, normalized via log curve Mass-Weighted Avg Mass-Weighted Avg
File Stability (Heat) N/A N/A Time since last commit, normalized via time distance Mass-Weighted Avg Mass-Weighted Avg
Verification Risk (Test Coverage) # of unverified execution paths, adjusted Sum of function counts Normalized via sigmoid function * Cross-File Test Tethers Mass-Weighted Avg Mass-Weighted Avg
Documentation Risk Exposure # of missing docstrings, comments, and readmes, adjusted Sum of function counts Normalized via sigmoid function * Bus Factor Mass-Weighted Avg Mass-Weighted Avg
API Exposure # of export/public modifiers, adjusted Sum of function counts + Public interfaces Normalized via ratio and volume * Network Blast Radius Mass-Weighted Avg Mass-Weighted Avg
Algorithmic DoS Exposure # of deep loops interacting with data/network, adjusted Sum of function counts Normalized via sigmoid function * Network Blast Radius Mass-Weighted Avg Mass-Weighted Avg
Logic Bomb Exposure # of conditions leading to payloads, adjusted Sum of function counts Normalized via sigmoid function * Taint Flow Tracking Mass-Weighted Avg Mass-Weighted Avg
Injection Surface Exposure # of inputs flowing to execution, adjusted Sum of function counts Normalized via sigmoid function * Taint Flow Tracking Mass-Weighted Avg Mass-Weighted Avg
Memory Corruption Exposure # of pointers/allocations minus cleanups, adjusted Sum of function counts Normalized via sigmoid function * ML Archetype Map Mass-Weighted Avg Mass-Weighted Avg
Obscured Payload Exposure # of obfuscation and intent triggers, adjusted Sum of function counts Normalized via sigmoid function * Biaxial Drift Mass-Weighted Avg Mass-Weighted Avg
Hardcoded Secrets Exposure N/A N/A Detected credential strings, normalized via sigmoid function Mass-Weighted Avg Mass-Weighted Avg

Custom Topological Scales

Certain metrics do not represent a "Safe to Dangerous" pipeline, but rather a difference in structural style within the graph. These bypass the Universal Spectrum and use custom rendering palettes.

  • Civil War (Tabs vs. Spaces): Checks for indentation consistency across the codebase.
  • 🟩 Green: Strictly uses Tabs.
  • 🟨 Yellow: Strictly uses Spaces.
  • 🟦 Blue: A chaotic, mixed indentation style (The "Warzone").

Expanding the Physics Framework

These documents outline the specific variables and mathematical normalization strategies that power the equations detailed in the matrix above.



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This documentation is part of the GitGalaxy Ecosystem, an AST-free, LLM-free heuristic knowledge graph engine.

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