Repository Analysis

xingpingcn/enhanced-FaaS-in-China

提升部署在cloudflare、vercel或netlify的网页在中国的访问速度和稳定性 cf优选域名 | cf优选ip | cloudflare | vercel | netlify | 加速 | 国内 | 中国 | 境内 | 大陆

22.9 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of xingpingcn/enhanced-FaaS-in-China, a Python project with 3,039 GitHub stars. SynthScan v2.0 examined 1,684 lines of code across 19 source files, recording 36 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 22.9 places this repository in the Moderate 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).

22.9
Adjusted Score
22.9
Raw Score
100%
Time Factor
2026-07-14
Last Push
3.0K
Stars
Python
Language
1.7K
Lines of Code
19
Files
36
Pattern Hits
2026-07-14
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

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

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 1LOW 35

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 36 distinct pattern matches across 4 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.

Unused Imports14 hits · 14 pts
SeverityFileLineSnippetContext
LOWmain.py2CODE
LOWset_DNS_record_to_HWcloud.py5CODE
LOWplatforms_to_test/cf.py4CODE
LOWplatforms_to_test/cf.py4CODE
LOWplatforms_to_test/cf.py5CODE
LOWplatforms_to_test/__init__.py1CODE
LOWplatforms_to_test/__init__.py2CODE
LOWplatforms_to_test/__init__.py3CODE
LOWplatforms_to_test/base_platform.py5CODE
LOWplatforms_to_test/base_platform.py6CODE
LOWplatforms_to_test/base_platform.py7CODE
LOWplatforms_to_test/base_platform.py8CODE
LOWplatforms_to_test/base_platform.py12CODE
LOWplatforms_to_test/base_platform.py14CODE
Hyper-Verbose Identifiers12 hits · 12 pts
SeverityFileLineSnippetContext
LOWdb.py74 async def just_refresh_last_test_time(self,isp:str,a_record:str):CODE
LOWdb.py149 async def get_now_down_but_alive_record(self, isp:str, limit:int = 10, offset:int = 0):CODE
LOWdb.py164 async def get_about_to_revive_record(self, isp:str, limit:int = 10, offset:int = 0):CODE
LOWset_DNS_record_to_HWcloud.py132 async def create_cname_record_with_batch_lines(self, subdomain, answer_dict: dict):CODE
LOWset_DNS_record_to_HWcloud.py149 async def update_batch_record_with_line(self, subdomain: str, answer_dict: dict):CODE
LOWplatforms_to_test/base_platform.py60 def _update_available_domains(self, current_time=None):CODE
LOWplatforms_to_test/base_platform.py147 async def wait_for_test_domain_available(self, timeout=None):CODE
LOWplatforms_to_test/base_platform.py170 async def wait_for_selected_domain_available(self, timeout=None):CODE
LOWplatforms_to_test/base_platform.py193 async def wait_for_any_domain_available(self, timeout=None):CODE
LOWplatforms_to_test/base_platform.py261 async def _set_domain_available_after_cooldown(self, domain):CODE
LOWplatforms_to_test/base_platform.py292 def is_all_test_domains_in_use(self):CODE
LOWplatforms_to_test/base_platform.py300 def is_all_selected_domains_in_use(self):CODE
Excessive Try-Catch Wrapping7 hits · 9 pts
SeverityFileLineSnippetContext
LOWdb.py58 except Exception as e:CODE
LOWdb.py72 except Exception as e:CODE
LOWdb.py82 except Exception as e:CODE
LOWdb.py101 except Exception as e:CODE
LOWplatforms_to_test/base_platform.py388 except Exception as e:CODE
LOWplatforms_to_test/base_platform.py554 except Exception as e:CODE
MEDIUMplatforms_to_test/base_platform.py538def run_crawler(test_url, test_ip, test_isps):CODE
Deep Nesting3 hits · 3 pts
SeverityFileLineSnippetContext
LOWplatforms_to_test/base_platform.py435CODE
LOWplatforms_to_test/base_platform.py575CODE
LOWplatforms_to_test/base_platform.py455CODE