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2.3.6. Signal Processing (Equations & 2nd Pass Calculations)

Overview: The Post-Processing Pipeline

The Signal Processor acts as GitGalaxy's core Physics Engine. Once the Splicer has carved the raw structural telemetry (the 60-Point SIGNAL_SCHEMA), the Physics Engine resolves these counts into meaningful, scaled insights—the 18-Point RISK_SCHEMA.

Rather than executing static math, the v6.2.0 Protocol introduces a multi-pass architecture. It evaluates a file not just in isolation, but against the physical reality of its surrounding neighborhood and the temporal history of the entire galaxy.

2.3.6.A. The Context vs. Entity Matrix (Domain Ontologies)

A file's risk profile changes drastically depending on where it lives. A C++ file in a firmware repository is expected; a C++ file hidden deep inside a frontend JavaScript repository is highly anomalous.

To catch these architectural Trojans, the engine utilizes the Context vs. Entity Matrix:

  • Native Ecosystems: The engine compares the file's language against the dominant language of its parent folder. If they match the same domain ontology (e.g., both are backend), the file is processed using standard native weights.
  • Alien Entities (Trojan Detection): If a severe context mismatch occurs, the file is classified as an "Alien." The engine dynamically injects severe risk multipliers. For example, a systems-level file (c, rust) hiding in a web neighborhood (javascript, html) receives massive multipliers to its logic_bomb and memory_corruption exposures, immediately flagging it as a severe architectural or security anomaly.

2.3.6.B. Standardization: The Tiered Physics Model

To ensure comparative fairness across different code "materials," the engine applies Linguistic Normalization. This accounts for the fact that explicit languages (like Rust) "broadcast" their safety, while implicit languages (like Shell) hide their risks.

The instrument applies specific Trust Constants based on the language's spectral class:

Tier Spectral Class Examples Physics Treatment
Tier 1 Explicit Rust, Go, Swift, Java Signals are trusted at face value. The Fidelity Coefficient is \(1.0\), and "Implicit Risk" is zero.
Tier 2 Structured Python, JS, C++ Translucent. A minor "Opacity Tax" is applied to account for potential runtime surprises.
Tier 3 Implicit Shell, SQL, Assembly Opaque. The "Fog of War" penalty adds a baseline phantom risk to all equations, requiring significantly higher defensive density to achieve a "Safe" rating.

2.3.6.C. The Documentation Bypass & Silo Risk

Pure literature files (markdown, plaintext) do not execute logic. To prevent them from skewing the galaxy's structural averages, the engine routes them through a strict Documentation Bypass.

While their logic metrics are safely zeroed out, they still undergo rigorous temporal and ownership physics:

  • Ownership Entropy: Calculates the Shannon Entropy of the file's Git commit history. A score of \(0\) indicates a single author (stable but siloed), while \(100\) indicates massive community distribution.
  • Silo Risk (The Bus Factor): Calculates the specific dependency the file has on its dominant author. If one developer wrote 95% of a critical architecture file, the Splicer flags a high Silo Risk, alerting management to critical "Bus Factor" vulnerabilities.

2.3.6.D. Global Synthesis & 2-Pass Normalization

Because temporal metrics (like commit frequency) vary wildly between repositories, a hardcoded churn threshold is useless. A file with 5 commits might be volatile in a dead repository but highly stable in an active one.

GitGalaxy solves this via Two-Pass Normalization:

  1. Pass 1 (Raw Extraction): The engine calculates the absolute Age (Stability) and the raw Churn Frequency (Commits over time) for every individual star.
  2. Pass 2 (The Global Curve): Once all files are processed, the _normalize_temporal_metrics engine scans the entire galaxy to find the "Volcano" (the absolute maximum churn frequency in the repository). It then applies a Logarithmic Curve (math.log1p) to scale every file's churn relative to that global ceiling. This guarantees that the UI gradients perfectly highlight the hottest files in the repository, regardless of the team's specific commit culture.

2.3.6.E. The Physics Engine: Weighted Asymmetry & Mass

In the final stage, the engine generates the high-fidelity Forensic Report. To prevent noise, it applies an Active Logic Mask, blinding the ranking algorithms to structural assets (like JSON configs or Markdown) so only true executable code competes for the "Highest Risk" spots.

Alongside ranking individual vectors (like highest Tech Debt or lowest Safety), the v6.2.0 engine calculates Cumulative Risk. It mathematically sums all 17 active exposure vectors (excluding formatting metrics like Civil War) to identify the absolute most dangerous, dense, and volatile artifacts in the repository.