sloptrace

Is your Python codebase getting messier? sloptrace measures it, and tracks how the numbers change over your git history.

It reports four things:

Everything is computed from the source with plain AST analysis (no LLM), so the same code always gives the same numbers. The approach is adapted from the SlopCodeBench paper (Orlanski et al., 2025).

Install

pip install git+https://github.com/sjtechdev/sloptrace.git

Requires Python 3.9+.

Usage

sloptrace .                                  # analyse the current checkout
sloptrace /path/to/repo --history            # ...and how it changed over git history
sloptrace . --history --every 20 --max-commits 15   # sample every 20th commit, at most 15
sloptrace . --history --since 2024-01-01 -o out.json
sloptrace . --show-literals                  # also list repeated literals (noisy, off by default)
sloptrace --help                             # all options

Which files are scanned: in a git repo, everything tracked or untracked but not ignored; otherwise the whole directory tree. Tests, docs, build output, virtualenvs and vendored code are skipped by default; change that with --exclude.

Development

git clone https://github.com/sjtechdev/sloptrace.git
cd sloptrace
pip install -e ".[test]"
pytest

Docs

Metric reference: the exact formula and a worked example for each metric.

Compared to scb-check

The paper's authors also publish scb-check. It supports many languages and flags code with lots of pattern rules. sloptrace only handles Python, but gives you evidence (which functions, which copies) and trends over history.

Its erosion number won't exactly match the paper's: the two tools count branches slightly differently, so don't compare absolute values with the paper's published baselines.