Churn measurement · AI code audit

The world's best code churn tool (CHG_SLOC + CHG_LLOC) adds AI-generated code detection!

CodeDelta computes physical and logical line-level churn (SLOC + LLOC) across two snapshots of a codebase, and audits the same code for machine-generated content — in over thirty languages.

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# churn + AI audit on two snapshots $ codedelta ./release-1.6.0 ./release-1.7.0 -o report.html --ai-audit scanning ...... 1,284 files matched across 31 languages aligning ...... two-pass logical diff complete SLOC changed 3,118 added 9,447 deleted 2,031 LLOC changed 1,902 added 5,613 deleted 1,144 # AI audit of added / changed code AI-AUDIT 312 files scanned 47 flagged as likely AI-generated 2,940 LLOC (34%) of new code matches machine-generation signals report written to report.html (per-file, verifiable)

Built on 15 years of churn-measurement heritage. Its predecessor was used by

AMD  ·  Cisco  ·  IBM  ·  Lockheed Martin  ·  Raytheon  ·  Nokia  ·  Ericsson  ·  Siemens  ·  Sony  ·  General Dynamics

CodeDelta is the successor to the Krakatau EPM metrics tools from PowerSoftware.com.

Two instruments in one

Churn measurement and AI audit, on the same codebase.

CodeDelta does two things from a single analysis: it measures exactly how much code changed between two versions, and it audits that code for machine-generated content. Both run locally, both produce verifiable per-file reports.

CHURNTwo-snapshot measurement

Quantifies added, deleted, changed, and unchanged lines between two versions of a project — per file and in aggregate.

CHURNPhysical & logical

Counts both SLOC (physical) and LLOC (statement-level) with per-language tokenisers, so reformatting doesn't inflate the result.

CHURNVerifiable output

Every classification is inspectable in a per-line report. The numbers can be audited, not just trusted.

AIDetects AI-generated code

Audits added and changed code for stylometric signals consistent with machine generation, and reports how much of a change is likely AI-written.

AIPer-file, flagged

Flags individual files and reports the proportion of new code matching machine-generation signals — surfacing where to focus human review.

AIHonest signals

Reported as signals for review, with the method and its limits documented in an open technical paper — not presented as infallible proof.

BOTH30+ languages

C/C++, Java, C#, Python, JavaScript/TypeScript, Go and more, detected automatically by extension.

BOTHLocal & offline

Runs entirely on your machine. No source code leaves your environment. Results accumulate in a local store for trend analysis.

BOTHOne report

Churn metrics and AI-audit findings appear together in a single per-file HTML report you can inspect and share internally.

The output

What CodeDelta produces.

A single run generates a per-file HTML report covering both churn and AI audit, a side-by-side diff viewer, and a summary overview — all inspectable, all local.

CodeDelta — Source Code Change Analysis
release-1.6.0 → release-1.7.0 · 1,284 files · 31 languages
ProjectChanged 218 Added 96Deleted 41Unchanged 929
3,118
CHG_SLOC
9,447
ADD_SLOC
2,031
DEL_SLOC
1,902
CHG_LLOC
filechgadddel
engine/parser.cpp2148831
core/matrix.cpp9614212
ui/dashboard.js614055
Churn report — per-file CHG / ADD / DEL across SLOC & LLOC.
AI Code Audit
312 files scanned · 47 flagged as likely AI-generated
Share of new code matching machine-generation signals
2,940 LLOC (34%) of added/changed code
fileAI-likelihood
utils/json_export.py88%
core/cache.py72%
engine/parser.cpp21%
AI audit — per-file likelihood that new code is machine-generated.
Diff Viewer — core/matrix.cpp
changed · +142 / −12 lines
41Matrix Matrix::add(const Matrix& o) const {
42− Matrix result(rows_, cols_);
42+ if (rows_ != o.rows_ || cols_ != o.cols_)
43+ throw std::invalid_argument("dim mismatch");
44+ Matrix result(rows_, cols_);
45 for (int i = 0; i < rows_*cols_; ++i)
46+ Matrix Matrix::multiply(const Matrix& o) const {
Diff viewer — line-by-line, every classification inspectable.
Project Overview
summary for project leads
14,592
TOTAL CRN
355
FILES TOUCHED
34%
AI-FLAGGED
Churn by type
changed 34% · added 48% · deleted 18%
Churn over last 6 snapshots
Overview — PM summary with trend across recorded runs.

Illustrative representations of CodeDelta output. Layout and figures shown for illustration.

Method

Two methods, documented openly.

Churn: CodeDelta aligns each file pair with a longest-common-subsequence algorithm, once over physical lines and once over a logical-statement token stream, with a second pass disambiguating repeated tokens using scope-qualified anchors.

AI audit: added and changed code is scored against stylometric signals associated with machine-generated source, and reported per file as a likelihood — explicitly as a signal for review, not a verdict.

Both methods, and their limitations, are documented in two open technical papers written to be independently verifiable.

Read the technical papers →

  • SLOC

    Physical lines

    Non-blank, non-comment source lines after whitespace normalisation.

  • LLOC

    Logical lines

    Statement-delimited units; invariant under most reformatting.

  • CHG

    Change classification

    Similarity-thresholded pairing of deletions and additions into modifications.

  • CRN

    Total churn

    Aggregate of changed, added, and deleted across the project.

  • AI

    AI-code audit

    Per-file likelihood that added/changed code is machine-generated, from stylometric signals.

At a glance
2
Instruments: churn + AI audit
30+
Languages auto-detected
100%
Local — no code uploaded
Mac / Win / Linux
Cross-platform

Evaluate it on your own codebase.

Download a time-limited trial license and run CodeDelta locally. No source code is transmitted.

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