| |
| """ |
| Figure 1: Language Coverage Map (9 languages → 12 IR kinds) |
| Figure 2: Compression Stability (1.5K → 10M functions) |
| For: "GraphLang: A Semantic Compression Layer Achieving 22.5x Reduction |
| Across 9 Programming Languages" — ArXiv 2026 |
| """ |
| import json |
|
|
| |
|
|
| COVERAGE = { |
| "Python": {"cst": 238, "ir_pct": 100, "status": "Production"}, |
| "Java": {"cst": 296, "ir_pct": 100, "status": "Production"}, |
| "JavaScript": {"cst": 242, "ir_pct": 100, "status": "Production"}, |
| "TypeScript": {"cst": 250, "ir_pct": 100, "status": "Production"}, |
| "C#": {"cst": 220, "ir_pct": 100, "status": "Production"}, |
| "Rust": {"cst": 290, "ir_pct": 100, "status": "Production"}, |
| "Go": {"cst": 199, "ir_pct": 100, "status": "Production"}, |
| "C": {"cst": 180, "ir_pct": 93, "status": "Stabilized"}, |
| "C++": {"cst": 300, "ir_pct": 93, "status": "Stabilized"}, |
| } |
|
|
| print("=" * 72) |
| print("FIGURE 1: Language Coverage Map") |
| print("=" * 72) |
| print() |
| print(f"{'Language':<14s} {'CST Types':>10s} {'IR Cov':>8s} {'Status':<14s}") |
| print("-" * 50) |
| total_cst = 0 |
| for lang, data in COVERAGE.items(): |
| total_cst += data["cst"] |
| bar = "█" * (data["ir_pct"] // 5) + ("░" if data["ir_pct"] < 100 else "") |
| print(f"{lang:<14s} {data['cst']:>8d} {data['ir_pct']:>3d}% {bar:20s} {data['status']:<14s}") |
| print("-" * 50) |
| avg_cov = sum(d["ir_pct"] for d in COVERAGE.values()) / len(COVERAGE) |
| print(f"{'TOTAL':<14s} {total_cst:>8d} {avg_cov:.0f}% avg → 12 IR kinds") |
| print() |
|
|
| |
|
|
| BENCHMARKS = [ |
| (1500, 33387, 1197, 27.9, 1.1), |
| (100000, 2172203, 96504, 22.5, 40.0), |
| (1000000, 21721250, 965048, 22.5, 20.0), |
| (10000000, 217210967, 9649257, 22.5, 203.0), |
| ] |
|
|
| print("=" * 72) |
| print("FIGURE 2: Compression Stability Across Scale") |
| print("=" * 72) |
| print() |
| print(f"{'Functions':>12s} {'Nodes':>12s} {'Unique':>10s} {'Ratio':>8s} {'Time':>8s}") |
| print("-" * 55) |
| for funcs, nodes, unique, ratio, secs in BENCHMARKS: |
| print(f"{funcs:>10,d} {nodes:>10,d} {unique:>8,d} {ratio:>4.1f}x {secs:>5.0f}s") |
| print("-" * 55) |
| print(f"{'Converges at':>12s} {'22.5x from':>24s} {'100K to':>18s} {'10M':>8s}") |
| print() |
|
|
| |
|
|
| KINDS_10M = { |
| "var": 62403662, |
| "const": 46315782, |
| "block": 27543852, |
| "return": 19999998, |
| "binop": 18947367, |
| "args": 10000002, |
| "function": 10000002, |
| "module": 10000002, |
| "if": 9824558, |
| "unary": 1578948, |
| } |
|
|
| print("=" * 72) |
| print("FIGURE 3: IR Kind Distribution (10M functions)") |
| print("=" * 72) |
| print() |
| max_count = max(KINDS_10M.values()) |
| for kind, count in sorted(KINDS_10M.items(), key=lambda x: -x[1]): |
| bar_len = int(count / max_count * 50) |
| bar = "█" * bar_len |
| pct = count / sum(KINDS_10M.values()) * 100 |
| print(f" {kind:12s} {count:>12,d} {bar} {pct:.0f}%") |
|
|
| print() |
| print(f" {'TOTAL':12s} {sum(KINDS_10M.values()):>12,d}") |
| print() |
|
|
| |
|
|
| print("=" * 72) |
| print("PAPER METADATA") |
| print("=" * 72) |
| print(f""" |
| Title: GraphLang: A Semantic Compression Layer Achieving 22.5x |
| Reduction Across 9 Programming Languages |
| |
| Author: Josué Argaña |
| Date: July 2026 |
| Repo: github.com/cripto-bot/graphlang |
| |
| Key Claims: |
| 1. 12 universal IR kinds capture complete computational intent |
| across 9 programming languages (7 at 100%, 2 at 93%). |
| 2. Compression ratio of 22.5x is mathematically stable from |
| 100K to 10M functions — converges, not degrades. |
| 3. Cross-language equivalence of 97% is achievable through |
| CST normalization alone, without ML or heuristics. |
| 4. The 12 IR kinds are finite and complete: only 16 structurally |
| unique ways to write an if statement exist across all languages. |
| |
| Suggested Venues: ICSE 2027, OOPSLA 2027, PLDI 2027 |
| Target: Tools & Demonstrations track (with live benchmark) |
| """) |
|
|
