Repository Analysis

deanmalmgren/textract

extract text from any document. no muss. no fuss.

4.2 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of deanmalmgren/textract, a HTML project with 4,662 GitHub stars. SynthScan v2.0 examined 13,076 lines of code across 126 source files, recording 53 pattern matches distributed across 7 syntactic categories. The overall adjusted score of 4.2 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).

4.2

Adjusted Score

4.2

Raw Score

100%

Time Factor

2026-07-11

Last Push

4.7K

Stars

HTML

Language

13.1K

Lines of Code

126

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 1LOW 52

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 53 distinct pattern matches across 7 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.

Hyper-Verbose Identifiers25 hits · 25 pts

Severity	File	Line	Snippet	Context
LOW	tests/test_special_filenames.py	49	def test_unbalanced_parentheses(self):	CODE
LOW	tests/test_special_filenames.py	54	def test_shell_injection_chars(self):	CODE
LOW	tests/test_special_filenames.py	70	def test_combined_special_chars(self):	CODE
LOW	tests/test_txt.py	15	def test_extensionless_filenames(self):	CODE
LOW	tests/test_pdf.py	95	def test_standardized_text_cli(self):	CODE
LOW	tests/test_pdf.py	99	def test_standardized_text_python(self):	CODE
LOW	tests/test_pdf.py	129	def test_unknown_method_raises(self):	CODE
LOW	tests/test_pdf.py	134	def test_pdftotext_fallback_to_pdfminer(self):	CODE
LOW	tests/test_exceptions.py	25	def test_unsupported_extension_cli(self):	CODE
LOW	tests/test_exceptions.py	38	def test_unsupported_extension_python(self):	CODE
LOW	tests/test_exceptions.py	47	def test_missing_filename_cli(self):	CODE
LOW	tests/test_exceptions.py	58	def test_missing_filename_python(self):	CODE
LOW	tests/test_exceptions.py	74	def test_missing_module_python(self):	CODE
LOW	tests/test_exceptions.py	98	def test_shell_parser_run_passes_startupinfo_on_windows(self):	CODE
LOW	tests/test_xlsx.py	10	def test_standardized_text_python(self):	CODE
LOW	tests/base.py	35	def _files_equal_ignore_blank_lines(file1: str, file2: str) -> bool:	CODE
LOW	tests/base.py	69	def _generate_file_diff_message(file1: str, file2: str) -> str:	CODE
LOW	tests/base.py	80	def _generate_bytes_diff_message(actual: bytes, expected: bytes, label: str) -> str:	CODE
LOW	tests/base.py	166	def standardized_text_filename(self):	CODE
LOW	tests/base.py	184	def test_standardized_text_cli(self):	CODE
LOW	tests/base.py	196	def test_standardized_text_python(self):	CODE
LOW	tests/test_epub.py	10	def _ignore_xml_as_html_warning(test_method):	CODE
LOW	tests/test_epub.py	32	def test_standardized_text_python(self):	CODE
LOW	textract/parsers/__init__.py	92	def _get_available_extensions():	CODE
LOW	textract/parsers/audio.py	28	def _pocketsphinx_signature_invalid() -> bool:	CODE

Unused Imports10 hits · 10 pts

Severity	File	Line	Context
LOW	textract/__init__.py	1	CODE
LOW	textract/exceptions.py	3	CODE
LOW	textract/parsers/jpg_parser.py	1	CODE
LOW	textract/parsers/mp3_parser.py	1	CODE
LOW	textract/parsers/wav_parser.py	1	CODE
LOW	textract/parsers/png_parser.py	1	CODE
LOW	textract/parsers/tiff_parser.py	1	CODE
LOW	textract/parsers/utils.py	5	CODE
LOW	textract/parsers/ogg_parser.py	1	CODE
LOW	textract/parsers/doc_parser.py	1	CODE

Modern Structural Boilerplate8 hits · 8 pts

Severity	File	Line	Snippet	Context
LOW	textract/__init__.py	5	__all__ = ["VERSION", "process"]	CODE
LOW	textract/parsers/jpg_parser.py	3	__all__ = ["Parser"]	CODE
LOW	textract/parsers/mp3_parser.py	3	__all__ = ["Parser"]	CODE
LOW	textract/parsers/wav_parser.py	3	__all__ = ["Parser"]	CODE
LOW	textract/parsers/png_parser.py	3	__all__ = ["Parser"]	CODE
LOW	textract/parsers/tiff_parser.py	3	__all__ = ["Parser"]	CODE
LOW	textract/parsers/gif_parser.py	54	__all__ = ["Parser"]	CODE
LOW	textract/parsers/ogg_parser.py	3	__all__ = ["Parser"]	CODE

Deep Nesting7 hits · 7 pts

Severity	File	Line	Context
LOW	textract/parsers/xlsx_parser.py	9	CODE
LOW	textract/parsers/html_parser.py	85	CODE
LOW	textract/parsers/html_parser.py	119	CODE
LOW	textract/parsers/odt_parser.py	30	CODE
LOW	textract/parsers/xls_parser.py	9	CODE
LOW	textract/parsers/audio.py	89	CODE
LOW	textract/parsers/epub_parser.py	40	CODE

Self-Referential Comments1 hit · 3 pts

Severity	File	Line	Snippet	Context
MEDIUM	textract/cli.py	42	# This function is necessary to enable autodocumentation of the script	COMMENT

Redundant / Tautological Comments1 hit · 2 pts

Severity	File	Line	Snippet	Context
LOW	textract/parsers/gif_parser.py	21	# Check if this is an animated GIF (n_frames > 1)	COMMENT

Fake / Example Data1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	tests/epub/raw_text.txt	4	Welcome here! All the text have ben generate with the Samuel L lorem ipsum.	CODE