Diffinite

Forensic source-code comparison tool for IP litigation and code audit.

Diffinite compares two directories of source code and produces professional PDF/HTML reports with syntax-highlighted side-by-side diffs. It uses Winnowing fingerprints (Schleimer et al., 2003 — the algorithm behind Stanford MOSS) for N:M cross-matching to detect code reuse even across renamed, split, or merged files.

Design Principle: Diffinite reports how similar and where similar. It does not classify the type of copying — that is the expert witness's job.

VS Code Extension

The recommended way to use Diffinite on Windows is through the VS Code extension, which bundles an embedded Python runtime — no separate Python installation required. On macOS/Linux the extension runs against a system Python (pip install diffinite, then set diffinite.pythonPath) — see Platform Support.

Features

Visual directory picker — Select two directories and configure options via a GUI panel
Real-time progress bar — Live percentage tracking during analysis
Pre-analysis time estimation — Scans file sizes upfront and estimates Simple/Deep mode duration
Dynamic CPU calibration — Benchmarks Phase 1 performance to refine Phase 2 time predictions
OOM defense — Warns before analyzing file pairs exceeding 5MB
Interactive tree viewer — Review matched pairs and selectively export
One-click PDF/HTML export — With Bates numbering, page numbers, and filename annotations

Install from Source

cd vscode-extension
npm install
npm run compile
# Press F5 in VS Code to launch Extension Development Host

CLI Installation

pip install diffinite

Or from source:

git clone https://github.com/nash-dir/diffinite.git
cd diffinite
pip install -e ".[dev]"

Requirements: Python ≥ 3.10

Dependencies: RapidFuzz, Pygments, xhtml2pdf, pypdf, reportlab, charset-normalizer

Platform Support

Component	Windows	Linux	macOS
CLI (`pip install diffinite`)	✅ supported	✅ tested in CI	◯ expected to work (pure Python; not in CI)
VS Code extension	✅ bundles an embedded Python runtime; primary tested target	△ via system Python (`pip install diffinite` + `diffinite.pythonPath`); not officially tested	△ same as Linux; not officially tested

The published VS Code extension is packaged for Windows (win32-x64) and ships the embedded runtime. On macOS/Linux, use the CLI directly, or point the extension at your own interpreter via the diffinite.pythonPath setting. The CLI itself is pure Python and platform-independent.

Quick Start

# Compare two directories → PDF report
diffinite original/ suspect/ -o report.pdf

# With comment stripping and Bates numbering (forensic use)
diffinite original/ suspect/ -o report.pdf \
    --strip-comments --bates-number --page-number --filename

# HTML report (single self-contained file, opens in browser)
diffinite original/ suspect/ --report-html report.html

How It Works

Diffinite runs a two-stage pipeline:

Stage 1: 1:1 File Matching (`simple` mode)

Fuzzy name matching — Pairs files across dir_a and dir_b using RapidFuzz string similarity (configurable threshold).
Comment stripping — Optionally removes comments using a 6-state finite state machine parser supporting 45+ file extensions.
Side-by-side diff — Computes line-by-line (or word-by-word) diffs using Python's difflib.SequenceMatcher with autojunk=True, a heuristic that drops high-frequency lines to speed up matching on large files (SequenceMatcher itself remains worst-case quadratic).
Report generation — Renders syntax-highlighted HTML diffs via Pygments, then converts to PDF with xhtml2pdf.

Stage 2: N:M Cross-Matching (`deep` mode, default)

Winnowing fingerprint extraction — Extracts position-independent code fingerprints using the Winnowing algorithm (K-gram → rolling hash → window selection).
Inverted index construction — Builds a hash-to-file mapping for all B-directory fingerprints.
Jaccard similarity computation — For each A-file, queries the index to find all B-files sharing fingerprints, then computes Jaccard similarity |A∩B| / |A∪B|.
Cross-match reporting — Appends an N:M similarity matrix to the report, showing which files from A are similar to which files in B.

Output Report

Cover Page

The cover page contains a summary table for each matched file pair:

Column	Description
File A / File B	Matched file paths
Name Sim.	Fuzzy filename similarity score (0–100)
Content Match	`difflib.SequenceMatcher.ratio()` — proportion of matching content. `1.0` = identical.
Added / Deleted	Number of lines (or words) added to or deleted from File A to produce File B.

Diff Pages

Each matched pair gets a side-by-side diff page with:

Green highlight — Lines present only in File B (additions)
Red highlight — Lines present only in File A (deletions)
Yellow highlight — Lines changed between A and B; in --by-word mode the changed words within are further marked (removed words struck through, added words bold)
Purple highlight — Lines moved from this position (--detect-moved)
Blue highlight — Lines moved to this position (--detect-moved)
No highlight — Identical lines (with configurable context folding)

Deep Compare Section

When running in deep mode (default), the report includes an N:M cross-matching table:

Column	Description
File A	Source file from directory A
Matched Files (B)	All files from directory B that share fingerprints above the Jaccard threshold
Shared Hashes	Count of Winnowing fingerprints the file pair has in common
Jaccard	`

Jaccard similarity is a well-defined set metric. Its interpretation depends on the domain, code size, and language. Diffinite reports the raw value without attaching qualitative labels.

Page Annotations

Option	Annotation	Position
`--page-number`	`Page 3 / 47`	Bottom-right
`--file-number`	`File 2 / 12`	Bottom-left
`--bates-number`	`TEST-000003-CONF`	Bottom-center
`--filename`	`com/example/Foo.java`	Top-right

CLI Reference

Positional Arguments

dir_a    Path to the original source directory (A)
dir_b    Path to the comparison source directory (B)

Execution Mode

Option	Default	Description
`--mode {simple,deep}`	`deep`	`simple` = 1:1 file matching only. `deep` = 1:1 + N:M Winnowing cross-matching.

Output Options

Option	Description
`--report-pdf PATH` (alias `-o`)	Generate a merged PDF report. Defaults to `report.pdf` when no `--report-*` flag is given.
`--report-html PATH`	Generate standalone HTML report (single file, no external deps)
`--report-md PATH`	Generate Markdown summary report
`--report-json PATH`	Generate machine-readable JSON report (used by VS Code extension)
`--no-merge`	Generate individual PDFs per file instead of one merged PDF
`--preserve-tree` / `--no-preserve-tree`	Preserve directory tree structure in individual output (default: on)
`--sort-by {filename,path,similarity,ratio}`	Sort matched pairs in the report. Default: insertion order (no sort).
`--sort-order {asc,desc}`	Sort direction (default: `asc`). Only effective with `--sort-by`.

PDF Font / CJK Rendering

Korean, Japanese, and Chinese text is rendered correctly in PDF output. By default the built-in xhtml2pdf CJK font (HYGothic-Medium) is used as a fallback; for a specific typeface, supply one of the options below. HTML output relies on the browser's native font fallback and needs no configuration.

Option	Description
`--pdf-lang {ko,ja,zh-cn,…}`	Auto-resolve the best OS-installed font for the given language from the built-in `pdf_fonts.json` map (Windows/macOS/Linux paths).
`--pdf-font PATH`	Absolute path to a `.ttf`/`.otf` font to embed via `@font-face` as the primary typeface. Takes precedence over `--pdf-lang`.

Extend or override language→font mappings by creating ~/.diffinite_fonts.json (same schema as the built-in map). User entries are merged over the built-ins per language.

Diff Options

Option	Default	Description
`--strip-comments`	off	Strip comments before comparison (6-state FSM parser, 45+ extensions)
`--by-word`	off	Compare by word instead of by line
`--squash-blanks`	off	Collapse runs of 3+ blank lines. ⚠️ Changes line numbers — not recommended for forensic line-tracing.
`--threshold N`	`60`	Fuzzy file-name matching threshold (0–100). Lower = more aggressive matching.
`--collapse-identical`	off	Fold unchanged code blocks (3 context lines around each change)
`--detect-moved`	off	Detect moved code blocks and highlight with distinct colors
`--encoding ENC`	`auto`	Force file encoding (e.g. `euc-kr`, `utf-8`). Default: auto-detect via charset-normalizer.

Deep Compare Options

Option	Default	Description
`--k-gram N`	`5`	K-gram size for Winnowing. Larger K = fewer but more specific fingerprints. (Schleimer 2003, §4.2)
`--window N`	`4`	Winnowing window size. Guarantees detection of any shared sequence ≥ `K+W−1` = 8 tokens.
`--threshold-deep N`	`5` raw / `93` normalize	Minimum Jaccard similarity (percent, 0–100) to include in results. The default differs by channel: raw uses 5; `--normalize` uses 93, calibrated for a false-positive rate ≤ 1% (see Normalize precision below). Pass a value to override.
`--normalize`	off	Normalize identifiers → `ID`, literals → `LIT` before fingerprinting. Improves Type-2 clone detection (renamed variables) but raises the false-positive rate on independent code — see Normalize precision. Matches below a 45-token floor are reported as inconclusive, and every normalize report discloses its measured false-positive rate.
`--lang-aware`	off	With `--normalize`, use language-aware normalization (Pygments lexer, falling back to per-language keyword sets) so keywords like Rust `fn`/`pub` or Go `func` are preserved instead of flattened to `ID`. Opt-in; produces fingerprints incompatible with the default channel. No effect without `--normalize`.
`--workers N`	`4`	Number of parallel worker processes for diff rendering and fingerprint extraction.

Forensic Options

Option	Default	Description
`--no-autojunk`	off	Disable `SequenceMatcher`'s autojunk heuristic. Treats all tokens equally — slower but more precise for forensic analysis.
`--max-index-entries N`	`10,000,000`	Memory cap for inverted index. Prevents OOM on large corpora. ~800MB at 10M entries.
`--max-file-size N`	`10.0`	Files larger than this (MB) bypass the in-memory text decode and fall back to a SHA-256 hash comparison (reported as match/no-match rather than a line diff). Prevents OOM/CPU lock on large binary/generated files.
`--hash`	off	Embed SHA-256 evidence integrity hashes for all analyzed files in the report.
`--uncompared-files {inline,separate,none}`	`inline`	Control how unmatched files are displayed: inline in the main report, written to a separate `*_uncompared.txt` file, or omitted.
`--bundle PATH`	—	Create an evidence bundle ZIP at `PATH` containing source files, generated reports, and the integrity manifest.
`--dir-alias-a TEXT` / `--dir-alias-b TEXT`	—	Display alias for directory A/B in reports (avoids exposing absolute paths).

Filtering & Advanced Options

Option	Default	Description
`--ignore-file PATH`	—	Path to a `.diffignore` file with glob patterns (e.g. `node_modules`, `*.pyc`) to exclude from analysis.
`--binary-handling {exclude,hash,error}`	`hash`	How to handle non-decodable (binary) files: skip, show SHA-256 match status, or report a decode error.
`--max-diff-html-size N`	`2.0`	Max HTML diff size (MB) before truncation. Prevents xhtml2pdf OOM/`RecursionError` on huge diffs.
`--metrics-only`	off	Phase 1 only: compute similarities and emit JSON, skipping HTML/PDF rendering.
`--filter-json PATH`	—	Phase 2: restrict output to the file-A paths listed in a JSON array (pairs with `--metrics-only`).
`--unreadable-log PATH`	—	Write the list of files that could not be read (permission errors) to `PATH`.

Page Annotation Options

Option	Description
`--page-number`	Show `Page n / N` at the bottom-right
`--file-number`	Show `File n / N` at the bottom-left
`--bates-number`	Stamp sequential Bates numbers at the bottom-center
`--bates-prefix TEXT`	Bates number prefix (e.g. `PLAINTIFF-`). Combined as: `{prefix}{number}{suffix}`
`--bates-suffix TEXT`	Bates number suffix (e.g. `-CONFIDENTIAL`)
`--bates-start N`	Starting Bates number (default: `1`). Useful for continuing numbering across reports.
`--filename`	Show filename at the top-right

Usage Examples

Basic IP Litigation Report

# Full forensic report with all annotations
diffinite plaintiff_code/ defendant_code/ -o exhibit_A.pdf \
    --strip-comments \
    --bates-number --bates-prefix=CASE2026- --bates-suffix=-CONFIDENTIAL \
    --bates-start 1 --page-number --file-number --filename \
    --collapse-identical --detect-moved --hash

Code Audit (Quick HTML)

# HTML report for browser viewing (no PDF dependency issues)
diffinite vendor_v1/ vendor_v2/ --report-html audit.html --strip-comments

Maximum Sensitivity (Type-2 Clones)

# Detect renamed-variable copies
diffinite original/ suspect/ -o report.pdf \
    --normalize --no-autojunk --strip-comments

Simple Mode (Fast, No Cross-Matching)

# 1:1 matching only — faster for quick comparisons
diffinite dir_a/ dir_b/ --mode simple -o quick_report.pdf

Multiple Output Formats

# Generate all formats at once
diffinite dir_a/ dir_b/ \
    --report-pdf report.pdf \
    --report-html report.html \
    --report-md report.md \
    --report-json report.json

Tuning Sensitivity

# Larger K-gram = fewer false positives, may miss short matches
diffinite dir_a/ dir_b/ --k-gram 7 --window 5

# Lower Jaccard threshold = show weaker matches (0–100 scale; raw default 5, 93 under --normalize)
diffinite dir_a/ dir_b/ --threshold-deep 2

# Stricter file name matching
diffinite dir_a/ dir_b/ --threshold 80

Comment Stripping Support

The --strip-comments flag removes comments using a 6-state finite state machine parser covering 45+ file extensions:

Extensions	Comment Styles
`.py`, `.pyw`	`# line` (docstrings preserved)
`.js`, `.jsx`, `.mjs`, `.ts`, `.tsx`	`// line`, `/* block */`, template literals, regex literals
`.java`, `.kt`, `.kts`, `.scala`, `.c`, `.cc`, `.cpp`, `.h`, `.hpp`, `.cs`, `.go`, `.rs`, `.swift`	`// line`, `/* block */`
`.html`, `.htm`, `.xml`, `.svg`	`<!-- block -->`
`.css`, `.scss`, `.less`	`/* block */`
`.sql`, `.ddl`, `.dml`, `.pks`, `.pkb`, `.plsql`, `.tsql`	`-- line`, `/* block */`
`.php`	`// line`, `# line`, `/* block */`
`.rb`	`# line`, `=begin … =end` block
`.pl`, `.pm`, `.sh`, `.bash`, `.zsh`, `.r`, `.yaml`, `.yml`, `.toml`	`# line`
`.lua`	`-- line`, `--[[ block ]]`

String and triple-quoted literals (including Python docstrings), template literals, and regex literals are deliberately preserved, not stripped — they are recognized only so that comment markers appearing inside them (e.g. // inside a string) are not mistaken for comments.

Project Structure

diffinite/
├── src/diffinite/
│   ├── cli.py              # CLI entry point & argument parsing
│   ├── pipeline.py         # Orchestration (simple/deep modes, parallel rendering)
│   ├── collector.py        # File collection & fuzzy name matching
│   ├── parser.py           # 6-state comment stripping FSM
│   ├── differ.py           # Diff computation, moved-block detection & HTML rendering
│   ├── fingerprint.py      # Winnowing fingerprint extraction
│   ├── deep_compare.py     # N:M cross-matching (inverted index + Jaccard)
│   ├── evidence.py         # SHA-256 integrity hashing & manifest generation
│   ├── models.py           # Data classes (DiffResult, DeepMatchResult, etc.)
│   ├── pdf_gen.py          # PDF/HTML report generation (xhtml2pdf)
│   └── languages/          # Per-language comment specs (45+ extensions)
├── vscode-extension/
│   ├── src/                # TypeScript extension source
│   │   ├── extension.ts    # Extension activation & command registration
│   │   ├── compareCommand.ts  # Directory selection, time estimation, pipeline orchestration
│   │   ├── dirScanner.ts   # Pre-analysis file scanning & OOM heuristic
│   │   ├── runner.ts       # Python backend spawner with progress bar integration
│   │   ├── optionsPanel.ts # GUI options webview (mode, comments, Bates, etc.)
│   │   ├── treeViewer.ts   # Interactive matched-pair tree for selective export
│   │   └── resultViewer.ts # HTML report preview inside VS Code
│   ├── bin/python/          # Embedded Python 3.12 runtime (gitignored)
│   └── package.json
├── example/                # Benchmark datasets (see below)
├── pyproject.toml
├── LICENSE                 # Apache 2.0
└── NOTICE

Benchmarks

Download the example datasets first, then run the benchmarks yourself:

python example/download_examples.py          # download all datasets
python example/download_examples.py --dataset aosp  # or download one

Pre-generated benchmark reports (Markdown) are in example/benchmark/.

1. Google v. Oracle — API Header Similarity

Why this dataset: The Oracle v. Google case is the landmark SSO (Structure, Sequence, Organization) copyright dispute. Google's Android reimplemented Java API declarations. The code bodies are independently written, but the API signatures are necessarily similar.

diffinite example/Case-Oracle/AOSP_Google example/Case-Oracle/OpenJDK_Oracle \
    --strip-comments --report-md example/benchmark/case_oracle.md

File	Match (difflib)	Deep Cross-Match (Jaccard)
`ArrayList.java`	9.0%	7.9%
`Collections.java`	4.5%	23.1%
`List.java`	6.3%	11.6%
`Math.java`	5.2%	6.3%
`String.java`	3.3%	6.8%

Observation: The line-level Match (difflib) scores stay under 10%, confirming the bodies are independently written. Deep Compare still surfaces shared Winnowing fingerprints — Jaccard 6–23% (highest on Collections.java) — because the API signatures and declarations are necessarily similar. High structural similarity alongside low line-level Match is precisely the SSO pattern at issue in the case: the same interface, independently implemented. Diffinite reports both numbers; interpreting them is the expert's job.

2. Eclipse Collections v. OpenJDK — Negative Control

Why this dataset: Eclipse Collections and OpenJDK solve similar problems (collection frameworks) but are developed by different teams with no code sharing. This is the expected baseline for independent work in the same domain.

diffinite example/Case-NegativeControl/Eclipse_Collections example/Case-NegativeControl/OpenJDK \
    --strip-comments --report-md example/benchmark/case_negative.md

File A	File B	Match	Deep Cross-Match
`StringIterate.java`	`String.java`	2.4%	—
`FastList.java`	`ArrayList.java`	1.5%	—

Observation: No cross-matches above the 5% Jaccard threshold. This is the correct result — independent projects should show near-zero similarity.

3. IR-Plag Case 01 — Known Plagiarism

Why this dataset: IR-Plag is a publicly available plagiarism corpus with labeled modification levels (L1=verbatim copy through L6=heavy restructuring).

diffinite example/plagiarism/case-01/original example/plagiarism/case-01/plagiarized \
    --normalize --strip-comments --report-md example/benchmark/plagiarism_case01.md

Original	Plagiarized	Jaccard
`T1.java`	`L2/04/hellow.java`	100.0%
`T1.java`	`L1/04/T1.java`	100.0%
`T1.java`	`L1/05/HelloWorld.java`	100.0%
`T1.java`	`L4/05/hellow.java`	56.2%
`T1.java`	`L5/02/Main.java`	38.1%
`T1.java`	`L6/07/PrintJava.java`	36.4%
`T1.java`	`L6/01/L6.java`	25.0%
`T1.java`	`L6/05/HelloWorld.java`	15.4%

Observation: Jaccard decreases as the plagiarism level increases (L1→L6). Verbatim and lightly-edited copies (L1–L3) score 100%. Heavily restructured copies (L5, L6) still show 15–38% shared fingerprints — well above the negative control baseline.

Normalize precision — false-positive rate

The negative control above runs without --normalize, while the IR-Plag run uses it — so neither shows what --normalize does to independent code. That cell matters: flattening every identifier to ID makes the language-forced skeleton of small, standard code (a textbook for-loop) identical regardless of author, so independent work and renamed copies can collapse to the same Jaccard.

It is measured, symmetrically, by the validation harness (tests/validation/error_rate.py; regenerate with python -m tests.validation.error_rate). The harness scores per file pair — every original file vs every candidate file — matching the runtime's per-file N:M decision and per-file token floor. On the IR-Plag corpus, where non-plagiarized/ submissions are independent answers to the same assignment (any similarity is a false positive):

Channel	False-positive rate @ threshold 5	Recall @ threshold 5
raw	100% (tiny files)	100%
normalize	100%	100%

The shipped --threshold-deep 5 is meaningless under --normalize: the false-positive rate is 100%. Two mitigations ship as a result (see example/validation/error_rate.md for the full curves and the operating-point characterization, and calibration.json for the machine-readable numbers):

Calibrated threshold — under --normalize, the default threshold is raised to 93, the operating point for false-positive ≤ 1%.
Inconclusive band — a normalize match whose smaller file is below a 45-token floor is reported as inconclusive: at that size, precision is unsalvageable at any useful threshold.

What the operating point honestly is (the calibration's own caveats, disclosed on every normalize report and in error_rate.md):

The false-positive rate is 0.95%, but that is 1 of 105 file pairs — Wilson 95% CI [0.17%, 5.2%]. It is not a known 1% rate; the upper bound exceeds 5%.
Recall is not uniform. At threshold 93: L1 65%, L2 46%, L3 21%, L4–L6 0%. The threshold reliably flags only near-verbatim copies; heavily restructured copies fall below it and need manual review. The pooled "~22%" should not be read as uniform sensitivity.
The 105 negatives derive from only 7 assignments (not i.i.d.), and nothing > 212 tokens was tested — the operating point is unvalidated on large files. At FP ≤ 1% the 45-token floor is non-binding on this corpus (it gates sub-floor matches at runtime, which this corpus does not exercise).

For non-JVM/Python/JS languages, --lang-aware reduces the collapse by preserving language keywords (e.g. Rust fn/pub) instead of flattening them to ID.

4. AOSP Framework — Same Codebase, Minor Edits

Why this dataset: Two versions of Android's Handler/Looper/Message framework. Small evolutionary changes between versions.

# Default run, comments included (the configuration shown in the sample image above)
diffinite example/aosp/left example/aosp/right

# With comment stripping (reproduces example/benchmark/aosp.md)
diffinite example/aosp/left example/aosp/right \
    --strip-comments --report-md example/benchmark/aosp.md

File	Match — comments included	Match — comments stripped
`Handler.java`	90.6%	88.6%
`Looper.java`	89.1%	90.0%
`Message.java`	96.0%	96.3%

Observation: High Match scores correctly reflect that these are minor revisions of the same codebase — and the scores stay high whether comments are included (the default) or stripped, showing the measurement is robust to that choice. The sample report image at the top of this README uses this dataset with comments included.

5. Unicode / i18n — Non-ASCII Source

Why this dataset: A small hand-authored A/B pair exercising non-ASCII filenames (계산기.py, 日本語.java), CJK / Cyrillic / Arabic identifiers and comments, RTL strings, and emoji — end to end through the report pipeline.

diffinite example/unicode/left example/unicode/right \
    --mode deep --normalize --pdf-lang ko \
    --report-pdf uni.pdf --report-html uni.html --report-md uni.md

What it verifies: the Unicode-aware tokenizer treats a CJK/Cyrillic identifier as a single token (so non-ASCII fingerprint density matches ASCII); comment-stripping handles non-ASCII; non-ASCII filenames render in every format; and the PDF embeds CJK glyphs (--pdf-lang ko selects a Korean font). Covered by tests/test_unicode_reports.py.

Winnowing Algorithm

Diffinite uses the Winnowing algorithm (Schleimer, Wilkerson, Aiken. "Winnowing: Local Algorithms for Document Fingerprinting." SIGMOD 2003), which also forms the basis of Stanford MOSS.

Pipeline: source → tokenize → K-gram → rolling hash → winnow → fingerprint set

The algorithm provides a density guarantee: any shared token sequence of length ≥ K + W − 1 (default: 8) will always be detected, regardless of its position in the file.

Parameters:

Parameter	Default	Rationale
`K` (k-gram)	`5`	Schleimer 2003 §4.2 recommended range. 5 consecutive tokens per fingerprint unit.
`W` (window)	`4`	Window of 4 fingerprints → minimum detectable sequence = 8 tokens.
`HASH_BASE`	`257`	Standard Rabin hash base (prime).
`HASH_MOD`	`2⁶¹ − 1`	Mersenne prime — efficient modular arithmetic, minimal collision probability.

Limitations

General-purpose tokenizer: Uses a single regex tokenizer for all languages, not language-specific parsers. Accuracy may vary across languages.
Position-independent: Winnowing fingerprints are order-independent within a window. Code with reordered functions may produce higher similarity than expected.
No corpus-based analysis: Each comparison is pairwise. There is no built-in corpus-wide frequency weighting (e.g., TF-IDF) to down-weight common idioms.
Binary and obfuscated code: Not supported. Diffinite operates on source code text only.
Not a legal opinion: Similarity scores are mathematical measurements, not legal conclusions. Professional review is required before use in any legal proceeding.

License

Apache License 2.0

See NOTICE for attribution.

Name		Name	Last commit message	Last commit date
Latest commit History 146 Commits
.github/workflows		.github/workflows
docs		docs
example		example
src/diffinite		src/diffinite
tests		tests
vscode-extension		vscode-extension
.gitignore		.gitignore
LICENSE		LICENSE
NOTICE		NOTICE
README.md		README.md
pyproject.toml		pyproject.toml

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Diffinite

VS Code Extension

Features

Install from Source

CLI Installation

Platform Support

Quick Start

How It Works

Stage 1: 1:1 File Matching (simple mode)

Stage 2: N:M Cross-Matching (deep mode, default)

Output Report

Cover Page

Diff Pages

Deep Compare Section

Page Annotations

CLI Reference

Positional Arguments

Execution Mode

Output Options

PDF Font / CJK Rendering

Diff Options

Deep Compare Options

Forensic Options

Filtering & Advanced Options

Page Annotation Options

Usage Examples

Basic IP Litigation Report

Code Audit (Quick HTML)

Maximum Sensitivity (Type-2 Clones)

Simple Mode (Fast, No Cross-Matching)

Multiple Output Formats

Tuning Sensitivity

Comment Stripping Support

Project Structure

Benchmarks

1. Google v. Oracle — API Header Similarity

2. Eclipse Collections v. OpenJDK — Negative Control

3. IR-Plag Case 01 — Known Plagiarism

Normalize precision — false-positive rate

4. AOSP Framework — Same Codebase, Minor Edits

5. Unicode / i18n — Non-ASCII Source

Winnowing Algorithm

Limitations

License

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Languages

Stage 1: 1:1 File Matching (`simple` mode)

Stage 2: N:M Cross-Matching (`deep` mode, default)

Packages