SWE Lab

Tooling to build, run, enrich, audit and fix SWE (a.k.a. coding agent) evaluation data.

Tasks

• Related-files annotation (src/swe_lab/pipelines/related_files/) — for each task instance, produce a ground-truth list of the code snippets a model needs to read to solve it. Shipped: 100 instances annotated & QA'd. See pipelines/related_files/README.md.

• Quality auditing (planned) — flag "skewed" eval examples that no longer measure real capability (ambiguous specs vs. overly-specific tests, broken environments, contamination, brittle graders), in the spirit of OpenAI's Separating signal from noise in coding evaluations. Not started; it will land as a sibling under pipelines/.

The overall roadmap and design live in docs/README.md.

Setup

Prerequisites

• uv for environment and dependency management
• direnv for auto-activating the environment
• Python 3.13 (uv will install it automatically if missing)

1. Clone

git clone https://github.com/Luolc/swe-lab.git
cd swe-lab

Optional — the --capture proxy mode compiles the standalone cc-reverse-proxy Go project. It is not a submodule: by default it is looked up as a sibling checkout next to this repo (../cc-reverse-proxy/reverse_proxy.go); clone it there, or point CC_REVERSE_PROXY_SRC at its reverse_proxy.go. The default stream capture needs none of this.

2. Set up the environment

uv sync          # create .venv and install all (incl. dev) dependencies
direnv allow     # auto-activate the venv on cd (uses .envrc)

If you don't use direnv, activate manually with source .venv/bin/activate.

Install the pre-commit hooks (ruff, pyink, isort, basedpyright, uv-lock):

uv run pre-commit install

3. Download the datasets

Dataset data files are gitignored and must be downloaded locally. See datasets/README.md for the list of available datasets and per-dataset download instructions.

Disclaimer

This is a personal project and is not affiliated with any company. The content does not reflect any specific company's projects, products or internal work.