Repository Analysis

wistbean/learn_python3_spider

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3.1 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of wistbean/learn_python3_spider, a Python project with 21,894 GitHub stars. SynthScan v2.0 examined 9,844 lines of code across 68 source files, recording 22 pattern matches distributed across 4 syntactic categories. The overall adjusted score of 3.1 places this repository in the Likely human-written 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).

3.1

Adjusted Score

3.1

Raw Score

100%

Time Factor

2026-05-17

Last Push

21.9K

Stars

Python

Language

9.8K

Lines of Code

Files

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 4LOW 18

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 22 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.

Over-Commented Block14 hits · 14 pts

Severity	File	Line	Snippet	Context
LOW	qiushibaike/qiushibaike/middlewares.py	61	# scrapy acts as if the downloader middleware does not modify the	COMMENT
LOW	qiushibaike/qiushibaike/middlewares.py	81	return None	COMMENT
LOW	qiushibaike/qiushibaike/settings.py	21	USER_AGENT = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/73.0.3683.86 Chrome	COMMENT
LOW	qiushibaike/qiushibaike/settings.py	41	#TELNETCONSOLE_ENABLED = False	COMMENT
LOW	qiushibaike/qiushibaike/settings.py	61	# Enable or disable extensions	COMMENT
LOW	qiushibaike/qiushibaike/settings.py	81	# each remote server	COMMENT
LOW	qiushibaike/qiushibaike/spiders/__init__.py	1	# This package will contain the spiders of your Scrapy project	COMMENT
LOW	stackoverflow/stackoverflow/middlewares.py	61	# scrapy acts as if the downloader middleware does not modify the	COMMENT
LOW	stackoverflow/stackoverflow/middlewares.py	81	return None	COMMENT
LOW	stackoverflow/stackoverflow/settings.py	21	# Obey robots.txt rules	COMMENT
LOW	stackoverflow/stackoverflow/settings.py	41	# Override the default request headers:	COMMENT
LOW	stackoverflow/stackoverflow/settings.py	61	#EXTENSIONS = {	COMMENT
LOW	stackoverflow/stackoverflow/settings.py	81	# Enable showing throttling stats for every response received:	COMMENT
LOW	stackoverflow/stackoverflow/spiders/__init__.py	1	# This package will contain the spiders of your Scrapy project	COMMENT

Self-Referential Comments4 hits · 12 pts

Severity	File	Line	Snippet	Context
MEDIUM	qiushibaike/qiushibaike/middlewares.py	18	# This method is used by Scrapy to create your spiders.	COMMENT
MEDIUM	qiushibaike/qiushibaike/middlewares.py	66	# This method is used by Scrapy to create your spiders.	COMMENT
MEDIUM	stackoverflow/stackoverflow/middlewares.py	18	# This method is used by Scrapy to create your spiders.	COMMENT
MEDIUM	stackoverflow/stackoverflow/middlewares.py	66	# This method is used by Scrapy to create your spiders.	COMMENT

Slop Phrases2 hits · 3 pts

Severity	File	Line	Snippet	Context
LOW	qiushibaike/qiushibaike/pipelines.py	5	# Don't forget to add your pipeline to the ITEM_PIPELINES setting	COMMENT
LOW	stackoverflow/stackoverflow/pipelines.py	5	# Don't forget to add your pipeline to the ITEM_PIPELINES setting	COMMENT

Unused Imports2 hits · 2 pts

Severity	File	Line	Snippet	Context
LOW	meizitu.py	4		CODE
LOW	qiushibaike/qiushibaike/spiders/qiushibaike_spider.py	2		CODE