# DST Framework — Declining Stress Threshold

**Live site:** https://idanreph.github.io/dst-framework/  
**Live demo:** https://idanreph.github.io/dst-framework/demo.html

> **Your system isn’t failing because you’re a bad engineer.**  
> **It’s failing because the structure allows damage to hide.**

DST is a **structural diagnostics framework + CI tool** for software systems.

It measures:

- **Θ (theta)** — real remaining capacity
- **κ (kappa)** — masking / hidden structural burden
- **σ (sigma)** — stress amplification loops
- **ρ (rho)** — real structural healing

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## What DST Actually Does

Add DST to your repo and every pull request can be evaluated for **structural impact**, not just style or correctness.

You do not just get “code quality.”

You get a structural read of the system:

```text
Θ = 42/100  → Plastic

Apparent = 71  → Gap = 29 ⚠️

💰 Cost of masking: $2.1M/year
⚠️ Stability is being maintained artificially (κ)
📉 dΘ/dt = -2.3 per PR → Declining

🟥 Structural replacement may become necessary soon

This is not a linter. This is a capacity measurement system for software.

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Install (60 seconds)

cp tools/dst-scanner.js ./
cp tools/dst-action.js ./

mkdir -p .github/workflows
cp .github/workflows/dst-diagnostic.yml .github/workflows/

git add . && git commit -m "Add DST diagnostic" && git push

Next PR → DST runs structural analysis.

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The Model

dΘ/dt = ρ − (σ − κ)

Where:

• σ (stress) → amplifiers that make failure scale faster
• ρ (healing) → real structural improvement
• κ (masking) → stabilizers that hide damage without removing it
• Θ (capacity) → how much change the system can still absorb

Key insight

A system can look stable while actually collapsing.

This is the observability gap:

Apparent ≠ Real

DST is built to measure that gap.

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The Four Regimes

Θ Range	Regime	Reality
75–100	🟢 Elastic	Structure dominates. Fast, safe change.
50–74	🟡 Plastic	System still works, but balance is fragile.
25–49	🟠 Late Plastic	Costs rise fast. Breakage becomes likely.
0–24	🔴 Residual	Masking dominates. Structural replacement approaches.

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What DST Detects

κ — masking (hidden debt)

• Silent error handling
• Retry logic that hides failure
• TODO / HACK accumulation
• Implicit state mutation
• “It works for now” code paths

σ — stress amplification

• Circular dependencies
• N+1 patterns
• Unbounded growth
• Hidden fan-out
• Failure multiplication loops

ρ — healing structure

• Pure functions
• Explicit boundaries
• Immutable state
• Observable failure surfaces
• Bounded, reversible behavior

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Why This Matters

Most teams feel this long before they can explain it:

• features slow down every sprint
• bugs multiply
• onboarding gets harder
• “quick fixes” make future work worse
• stability starts to feel expensive

DST turns that into:

measurable capacity, trajectory, and cost

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Real-World Results

DST v4.5-final has been applied to major systems:

React         → Θ ~100 → Elastic
Next.js       → Θ ~65  → Plastic
Log4Shell     → Θ ~8   → Residual
Express       → Θ ~0   → Residual
web-llm-chat  → Θ ~0   → Residual

See: docs/examples

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v4.5-final Adds

DST v4.5-final keeps the same structural math while adding stronger enforcement and measurement:

1. κ_i expiration contracts Transitional complexity can be annotated with:

   @dst-kappa-i: expires YYYY-MM-DD
   

   and tracked through CI.

2. σ environment scaling DST_DATA_SCALE changes amplifier weight by deployment context:

   small x0.5 · medium x1.0 · large x2.0 · hyperscale x4.0
   

3. AST-assisted engine AST-powered N+1 and silent-catch detection run alongside regex where supported.

4. ΔΘ gate logic PRs can be evaluated not only by score, but by trajectory.

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DST Compass

DST Compass is the interactive diagnostic layer for the framework.

It translates the model into guided structural diagnosis for humans:

• regime detection
• contradictions / gaps
• confidence analysis
• challenge synthesis
• report generation
• hashable diagnostic output

Compass is the human-facing side of the same framework: scanner for code, Compass for systems thinking.

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Repo Structure

• tools/ → scanner + GitHub Action
• .github/workflows/ → CI workflow
• docs/examples/ → real-world case studies and rescans
• docs/math/ → axioms, equations, proof spine
• docs/pitch/ → engineering / CTO explanations
• docs/research/ → theory and related papers
• docs/index.html → live site entry
• docs/demo.html → live demo / runnable page

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The Theory

DST is not just a code tool. It is a general law for constrained dynamic systems.

Related theory work:

• SSRN 6434119 — core DST framework
• SSRN 6434258 — financial system application

The software scanner is one application of the wider theory.

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Origin

DST started from a macro observation:

systems that appeared stable while their real capacity was declining.

That logic turned out to apply far beyond finance:

• codebases
• AI systems
• organizations
• biological systems
• engineered systems under hidden load

See the theory site and research docs for the formal model.

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What Happens Next

Run it on your repo.

If it’s wrong, you’ll ignore it. If it’s right, you won’t be able to unsee it.

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Contributing

Found a missed pattern? Improved a detector? Want to refine the math or CI behavior?

Open an issue or contribute directly.

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License

No license yet.

The framework is published and timestamped.

Use it. Break it. Improve it.

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ρ heals · κ hides · σ kills