Repository Analysis

boyter/scc

Sloc, Cloc and Code: scc is a very fast accurate code counter with complexity calculations and COCOMO estimates written in pure Go

3.6 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of boyter/scc, a Go project with 8,532 GitHub stars. SynthScan v2.0 examined 63,335 lines of code across 187 source files, recording 66 pattern matches distributed across 11 syntactic categories. The overall adjusted score of 3.6 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).

3.6
Adjusted Score
3.6
Raw Score
100%
Time Factor
2026-07-14
Last Push
8.5K
Stars
Go
Language
63.3K
Lines of Code
187
Files
66
Pattern Hits
2026-07-14
Scan Date
0.09
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 2HIGH 15MEDIUM 18LOW 31

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 66 distinct pattern matches across 11 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.

Cross-Language Confusion13 hits · 92 pts
SeverityFileLineSnippetContext
HIGHexamples/performance_tests/create_performance_test.py55 if (o.toString().length() == word.length()) {CODE
HIGHexamples/performance_tests/create_performance_test.py55 if (o.toString().length() == word.length()) {CODE
HIGHexamples/performance_tests/create_performance_test.py56 bestMatch = o.toString();CODE
HIGHexamples/performance_tests/create_performance_test.py83 if (o.toString().length() == word.length()) {CODE
HIGHexamples/performance_tests/create_performance_test.py83 if (o.toString().length() == word.length()) {CODE
HIGHexamples/performance_tests/create_performance_test.py84 bestMatch = o.toString();CODE
HIGHexamples/performance_tests/create_performance_test.py110 for (int i = 1; i < word.length() + 1; i++) {CODE
HIGHexamples/performance_tests/create_performance_test.py114 closeWords.add(sb.toString());CODE
HIGHexamples/performance_tests/create_performance_test.py118 for (int i = 1; i < word.length(); i++) {CODE
HIGHexamples/performance_tests/create_performance_test.py122 closeWords.add(sb.toString());CODE
HIGHexamples/performance_tests/create_performance_test.py126 closeWords.add(sb.toString());CODE
HIGHexamples/performance_tests/create_performance_test.py12 private Map<String, Integer> dictionary = null;CODE
HIGHexamples/performance_tests/create_performance_test.py31 if (word == null || word.trim().isEmpty()) {CODE
Self-Referential Comments16 hits · 62 pts
SeverityFileLineSnippetContext
MEDIUMexamples/performance_tests/create_folders_with_files.py15# Create a directory thats quite deep and put a 10000 files at the endCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py23# Create a directory thats quite deep and put 100 files in each folderCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py33# Create a directory that has a single level and put 10000 files in itCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py41# Create a directory that has a two levels with 10000 directories in the second with a single file in eachCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py51# Create a directory that with 10 subdirectories and 1000 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py62# Create a directory that with 20 subdirectories and 500 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py73# Create a directory that with 5 subdirectories and 2000 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_folders_with_files.py84# Create a directory that with 100 subdirectories and 100 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py165# Create a directory thats quite deep and put a 10000 files at the endCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py173# Create a directory thats quite deep and put 100 files in each folderCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py183# Create a directory that has a single level and put 10000 files in itCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py191# Create a directory that has a two levels with 10000 directories in the second with a single file in eachCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py201# Create a directory that with 10 subdirectories and 1000 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py212# Create a directory that with 20 subdirectories and 500 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py223# Create a directory that with 5 subdirectories and 2000 files in eachCOMMENT
MEDIUMexamples/performance_tests/create_performance_test.py234# Create a directory that with 100 subdirectories and 100 files in eachCOMMENT
Hallucination Indicators2 hits · 25 pts
SeverityFileLineSnippetContext
CRITICALexamples/minified/jquery-3.1.1.min.js4void 0!==c?null===c?void r.removeAttr(a,b):e&&"set"in e&&void 0!==(d=e.set(a,c,b))?d:(a.setAttribute(b,c+""),c):e&&"get"CODE
CRITICALexamples/minified/app-5cddf2000f4491a89a40.js1(window.webpackJsonp=window.webpackJsonp||[]).push([[2],[function(e,t,n){"use strict";e.exports=n(184)},function(e,t,n){CODE
Over-Commented Block24 hits · 24 pts
SeverityFileLineSnippetContext
LOWconfig.go461 // --no-fold-authors is read back via cmd.PersistentFlags().GetBool in Run, soCOMMENT
LOWtest-all.sh41#if ./scc "examples/language/" --format cloc-yaml -o .tmp_scc_yaml >/dev/null && python <<EOSCOMMENT
LOWtest-all.sh61# rm -f .tmp_scc_yamlCOMMENT
LOWREADME.md1241COMMENT
LOW.goreleaser.yml41COMMENT
LOWregression_test.go21// changed by the registerFlags refactor, the shared @file tokenizer and theCOMMENT
LOWregression_test.go561// TestRegressionConfigMinFlagClassifies fills a behavioural gap the existingCOMMENT
LOWcmd/badges/example.py261 # j = json.loads(content)COMMENT
LOWprocessor/formatters_test.go741 Files = falseCOMMENT
LOWprocessor/formatters_test.go981# TYPE scc_lines gaugeCOMMENT
LOWprocessor/report_render.go101// - bare `--report` and the file doesn't exist: proceed.COMMENT
LOWprocessor/cognitive_nesting_test.go1// SPDX-License-Identifier: MITCOMMENT
LOWprocessor/cocomo.go1// SPDX-License-Identifier: MITCOMMENT
LOWprocessor/structs.go41 Start string `json:"start"`COMMENT
LOWprocessor/history_authors.go21// row in the tabular table.COMMENT
LOWprocessor/history.go101// Mailmap is the parsed .mailmap from the HEAD tree, if present.COMMENT
LOWprocessor/history.go461// (author rollup, hotspots) depend on renames arriving as a single changeCOMMENT
LOWprocessor/report.go21// DefaultReportName is the file name used when --report is invoked withoutCOMMENT
LOWprocessor/workers_issue466_test.go1// SPDX-License-Identifier: MITCOMMENT
LOWprocessor/workers.go601 }COMMENT
LOWexamples/language/hcl.hcl1#!/usr/bin/env packer build --forceCOMMENT
LOWexamples/issue339/objectivec.m1//COMMENT
LOWexamples/issue339/objectivec.m821//未调用COMMENT
LOW.github/workflows/codeql-analysis.yml41COMMENT
Synthetic Comment Markers2 hits · 12 pts
SeverityFileLineSnippetContext
HIGHREADME.md656- < 55% (High Boilerplate): High repetition. Likely due to mandatory error handling, auto-generated code, or verbose conCODE
HIGHprocessor/processor.go654// LoadLanguageFeature will load a single feature as requested given the nameCOMMENT
AI Slop Vocabulary2 hits · 6 pts
SeverityFileLineSnippetContext
MEDIUMexamples/minified/app-5cddf2000f4491a89a40.js1(window.webpackJsonp=window.webpackJsonp||[]).push([[2],[function(e,t,n){"use strict";e.exports=n(184)},function(e,t,n){CODE
MEDIUMexamples/minified/app-5cddf2000f4491a89a40.js1(window.webpackJsonp=window.webpackJsonp||[]).push([[2],[function(e,t,n){"use strict";e.exports=n(184)},function(e,t,n){CODE
Fake / Example Data2 hits · 2 pts
SeverityFileLineSnippetContext
LOWexamples/minified/jquery.dataTables.min.js41g):j+g,b=h("<div/>",{id:!f.f?c+"_filter":null,"class":b.sFilter}).append(h("<label/>").append(j)),f=function(){var b=!thCODE
LOWexamples/minified/app-5cddf2000f4491a89a40.js1(window.webpackJsonp=window.webpackJsonp||[]).push([[2],[function(e,t,n){"use strict";e.exports=n(184)},function(e,t,n){CODE
Unused Imports2 hits · 2 pts
SeverityFileLineSnippetContext
LOWcmd/badges/example.py10CODE
LOWexamples/oneline.py1CODE
Verbosity Indicators1 hit · 2 pts
SeverityFileLineSnippetContext
LOWprocessor/workers.go528 // If so we need to check if where we are falls into these conditionsCOMMENT
Excessive Try-Catch Wrapping1 hit · 1 pts
SeverityFileLineSnippetContext
LOWcmd/badges/example.py100 except Exception:CODE
Deep Nesting1 hit · 1 pts
SeverityFileLineSnippetContext
LOWcmd/badges/example.py19CODE