serde-rs/json

7.5

Adjusted Score

7.5

Raw Score

100%

Time Factor

2026-07-28

Last Push

5.6K

Stars

Rust

Language

24.0K

Lines of Code

Files

191

Pattern Hits

2026-08-02

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 0LOW 191

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 191 distinct pattern matches across 2 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 Block174 hits · 157 pts

Severity	File	Line	Snippet	Context
LOW	Cargo.toml	61	# Make serde_json::Map use a representation which maintains insertion order.	COMMENT
LOW	Cargo.toml	81	arbitrary_precision = []	COMMENT
LOW	src/number.rs	161	/// Represents the number as f64 if possible. Returns None otherwise.	COMMENT
LOW	src/number.rs	221	}	COMMENT
LOW	src/number.rs	281		COMMENT
LOW	src/error.rs	101	pub fn is_data(&self) -> bool {	COMMENT
LOW	src/error.rs	121	/// use std::io::{self, ErrorKind, Read};	COMMENT
LOW	src/error.rs	141	/// Err(error) => {	COMMENT
LOW	src/error.rs	181	///	COMMENT
LOW	src/error.rs	201	/// }	COMMENT
LOW	src/lib.rs	1	//! # Serde JSON	COMMENT
LOW	src/lib.rs	21	//! There are three common ways that you might find yourself needing to work	COMMENT
LOW	src/lib.rs	41	//! representation. This data structure is [`serde_json::Value`][value].	COMMENT
LOW	src/lib.rs	61	//!	COMMENT
LOW	src/lib.rs	81	//! println!("Please call {} at the number {}", v["name"], v["phones"][0]);	COMMENT
LOW	src/lib.rs	101	//! containing a JSON string and its JSON representation is `"John Doe"`.	COMMENT
LOW	src/lib.rs	121	//! use serde::{Deserialize, Serialize};	COMMENT
LOW	src/lib.rs	141	//! }"#;	COMMENT
LOW	src/lib.rs	161	//! automatically interpret the input data as a `Person` and produce informative	COMMENT
LOW	src/lib.rs	181	//!	COMMENT
LOW	src/lib.rs	201	//! ```	COMMENT
LOW	src/lib.rs	221	//! "name": full_name,	COMMENT
LOW	src/lib.rs	241	//!	COMMENT
LOW	src/lib.rs	261	//!	COMMENT
LOW	src/lib.rs	281	//! without the rest of the Rust standard library. Disable the default "std"	COMMENT
LOW	src/raw.rs	21	///	COMMENT
LOW	src/raw.rs	41	/// #[derive(Deserialize)]	COMMENT
LOW	src/raw.rs	61	/// let input: Input = serde_json::from_str(input)?;	COMMENT
LOW	src/raw.rs	81	/// The typical usage of `RawValue` will be in the borrowed form:	COMMENT
LOW	src/raw.rs	101	///	COMMENT
LOW	src/raw.rs	181	/// except that we avoid an allocation and memcpy if both of the following	COMMENT
LOW	src/raw.rs	201	///	COMMENT
LOW	src/raw.rs	221	/// // SAFETY: `json` was produced by serde_json's own serializer, so it is	COMMENT
LOW	src/raw.rs	241	/// ```	COMMENT
LOW	src/raw.rs	281	/// Convert a `T` into a boxed `RawValue`.	COMMENT
LOW	src/raw.rs	301	/// b: u32,	COMMENT
LOW	src/raw.rs	321	/// This conversion can fail if `T`'s implementation of `Serialize` decides to	COMMENT
LOW	src/ser.rs	1681	/// correctly formatted but unspecified numerical value.	COMMENT
LOW	src/ser.rs	1701	///	COMMENT
LOW	src/ser.rs	2181	{	COMMENT
LOW	src/de.rs	41	impl<'de, R> Deserializer<R>	COMMENT
LOW	src/de.rs	161	StreamDeserializer {	COMMENT
LOW	src/de.rs	181	/// *This method is only available if serde_json is built with the	COMMENT
LOW	src/de.rs	201	/// carefully_drop_nested_arrays(value);	COMMENT
LOW	src/de.rs	1561	/// whether the bytes represent a valid UTF-8 string.	COMMENT
LOW	src/de.rs	1581	///	COMMENT
LOW	src/de.rs	1601	///	COMMENT
LOW	src/de.rs	1621	/// use serde_bytes::ByteBuf;	COMMENT
LOW	src/de.rs	2341	///	COMMENT
LOW	src/de.rs	2401	/// let de = serde_json::Deserializer::from_slice(data);	COMMENT
LOW	src/de.rs	2521	///	COMMENT
LOW	src/de.rs	2541	/// # Example	COMMENT
LOW	src/de.rs	2561	/// let file = File::open(path)?;	COMMENT
LOW	src/de.rs	2581	/// ```	COMMENT
LOW	src/de.rs	2601	/// fn main() {	COMMENT
LOW	src/de.rs	2621	/// type.	COMMENT
LOW	src/de.rs	2641	/// fingerprint: String,	COMMENT
LOW	src/de.rs	2661	/// structure expected by `T`, for example if `T` is a struct type but the input	COMMENT
LOW	src/de.rs	2681	/// #[derive(Deserialize, Debug)]	COMMENT
LOW	src/de.rs	2701	///	COMMENT
114 more matches not shown…

Fake / Example Data17 hits · 22 pts

Severity	File	Line	Snippet	Context
LOW	README.md	30	"name": "John Doe",	CODE
LOW	README.md	94	"name": "John Doe",	CODE
LOW	README.md	120	the output looks like `Please call "John Doe" at the number "+44 1234567"`. The	CODE
LOW	README.md	122	string and its JSON representation is `"John Doe"`. Printing as a plain string	CODE
LOW	README.md	162	"name": "John Doe",	CODE
LOW	README.md	222	"name": "John Doe",	CODE
LOW	README.md	252	let full_name = "John Doe";	CODE
LOW	src/lib.rs	8	//! "name": "John Doe",	COMMENT
LOW	src/lib.rs	69	//! "name": "John Doe",	COMMENT
LOW	src/lib.rs	99	//! above, the output looks like `Please call "John Doe" at the number "+44	COMMENT
LOW	src/lib.rs	101	//! containing a JSON string and its JSON representation is `"John Doe"`.	COMMENT
LOW	src/lib.rs	135	//! "name": "John Doe",	COMMENT
LOW	src/lib.rs	188	//! "name": "John Doe",	COMMENT
LOW	src/lib.rs	216	//! let full_name = "John Doe";	COMMENT
LOW	src/value/mod.rs	14	//! "name": "John Doe",	COMMENT
LOW	src/value/mod.rs	42	//! let full_name = "John Doe";	COMMENT
LOW	src/value/mod.rs	68	//! "name": "John Doe",	COMMENT

Analysis Overview

What These Metrics Mean

Score History

Severity Breakdown

Directory Score Breakdown

Pattern Findings