Repository Analysis

yuanzl77/IPTV

每天自动更新IPTV直播源,支持IPV4/IPV6双栈访问!自定义频道,高质量直播源,❌不含有广告。Automatically update IPTV live streaming sources every day, supporting IPV4/IPV6 dual stack access! Custom channels, high-quality live streaming sources, ❌ Does not contain advertisements.

53.5 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of yuanzl77/IPTV, a Python project with 2,101 GitHub stars. SynthScan v2.0 examined 1,514 lines of code across 8 source files, recording 42 pattern matches distributed across 6 syntactic categories. The overall adjusted score of 53.5 places this repository in the Strong AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

53.5
Adjusted Score
53.5
Raw Score
100%
Time Factor
2026-08-24
Last Push
2.1K
Stars
Python
Language
1.5K
Lines of Code
8
Files
42
Pattern Hits
2026-08-29
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 12LOW 30

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 42 distinct pattern matches across 6 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators12 hits · 50 pts
SeverityFileLineSnippetContext
MEDIUMconfig.py1# ═══════════════════════════════════════════════════════════════════COMMENT
MEDIUMconfig.py4# ═══════════════════════════════════════════════════════════════════COMMENT
MEDIUMconfig.py6# ── IP 优先级 ───────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py10# ── 源优先级 ────────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py17# ── 订阅源 ───────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py25# ── 酒店源 ────────────────────────────────────────────COMMENT
MEDIUMconfig.py37# ── URL 黑名单 ───────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py45# ── 公告条目 ────────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py65# ── EPG 电子节目单 ────────────────────────────────────────────────────COMMENT
MEDIUMconfig.py74# ── 质量检测 — HTTP 快筛 ─────────────────────────────────────────────COMMENT
MEDIUMconfig.py82# ── 质量检测 — FFprobe 中度探测 ───────────────────────────────────────COMMENT
MEDIUMconfig.py102# ── 深度探测配置 ───────────────────────────────────────────────────────COMMENT
Deep Nesting13 hits · 13 pts
SeverityFileLineSnippetContext
LOWcheck.py142CODE
LOWcheck.py448CODE
LOWcheck.py522CODE
LOWfetch_hotel.py115CODE
LOWfetch_hotel.py133CODE
LOWmain.py14CODE
LOWmain.py32CODE
LOWmain.py66CODE
LOWmain.py126CODE
LOWmain.py176CODE
LOWmain.py215CODE
LOWmain.py271CODE
LOWmain.py308CODE
Excessive Try-Catch Wrapping9 hits · 10 pts
SeverityFileLineSnippetContext
LOWcheck.py76 except Exception as e:CODE
LOWcheck.py136 except Exception as e:CODE
LOWcheck.py220 except Exception as e:CODE
LOWcheck.py355 except Exception as e:CODE
LOWcheck.py376 except Exception:CODE
LOWfetch_hotel.py21 except Exception as e:CODE
LOWfetch_hotel.py31 except Exception as e:CODE
LOWfetch_hotel.py146 except Exception as e:CODE
LOWmain.py55 except Exception as e:CODE
Over-Commented Block4 hits · 4 pts
SeverityFileLineSnippetContext
LOWconfig.py1# ═══════════════════════════════════════════════════════════════════COMMENT
LOWconfig.py41 "epg.pw/stream/",COMMENT
LOWconfig.py81COMMENT
LOWconfig.py101COMMENT
Modern Structural Boilerplate2 hits · 2 pts
SeverityFileLineSnippetContext
LOWcheck.py15logger = logging.getLogger(__name__)CODE
LOWfetch_hotel.py11logger = logging.getLogger(__name__)CODE
Hyper-Verbose Identifiers2 hits · 2 pts
SeverityFileLineSnippetContext
LOWcheck.py370def _shutdown_ffprobe_executor():CODE
LOWmain.py253def _print_domain_suggestions(fail_domains: dict):CODE