osolmaz/aacr-bench-harbor

AACR-Bench Harbor

AACR-Bench Harbor packages AACR-Bench as independent Harbor tasks for code-review agents. Each task checks out one pull request at its pinned head commit, keeps the base as aacr-base, and grades a structured findings file with AACR-Bench's path, side, line, and semantic matching stages.

This repository is not a fork of AACR-Bench or Harbor.

Install

Requirements:

• Python 3.12 or newer
• uv
• Git
• Harbor
• Docker for local task runs, or Harbor's hf-sandbox extra for Hugging Face Sandboxes

git clone https://huggingface.co/datasets/osolmaz/aacr-bench-harbor
cd aacr-bench-harbor
uv sync --locked --dev

The Hugging Face dataset repository is the single canonical repository. It contains the code and immutable source packs needed to build tasks without cloning upstream repositories. If a Git client leaves large files as pointers, hydrate the exact corpus revision:

uv run aacr-source-pack hydrate \
  --manifest source-packs/manifest.json \
  --revision b1faf33764460fa7246e92bf9d3e89ba804ad17e

Generate tasks

The default source is pinned to AACR-Bench commit b3072489eace26efca8bcf2b1ac6a24ba64f82c1 and SHA-256 d8683cb240249bc4e0aff6428802bdffa7b7573ace600552cab1cd0cb7e905c9.

Generate one released sealed task from the local source packs:

uv run aacr-bench \
  --output-dir datasets/aacr-bench \
  --source-pack-manifest source-packs/manifest.json \
  --task-ids appwrite-appwrite-pr-9999-710b8bda565c

Generate any released task with --task-ids. Existing task directories are never replaced unless --overwrite is present. Task generation verifies the archive, commit, tree, diff, clean checkout, and absence of Git remotes before writing Harbor files. It performs no repository network access.

Maintainers can verify every released pack with:

uv run aacr-source-pack verify --manifest source-packs/manifest.json

Generated Harbor datasets are ignored by Git.

Run with Harbor

Verify generated tasks with the oracle first:

harbor run -p datasets/aacr-bench --agent oracle

Run GPT-5.6 Luna through Codex's native review harness:

harbor run \
  -p datasets/aacr-bench \
  --agent agents.codex_review:CodexReview \
  --model openai/gpt-5.6-luna

OPENAI_API_KEY must already be present in the invoking environment. The custom agent uses an isolated temporary CODEX_HOME, calls codex review --base aacr-base, validates the structured review, and writes /workspace/aacr-review.json outside the read-only review session.

For Hugging Face Sandbox execution, install Harbor's hf-sandbox extra and add:

-e hf-sandbox \
--ek flavor=cpu-basic \
--ek forward_hf_token=true

The benchmark image uses the official release tag node:24.7.0-bullseye. Its recorded OCI index digest is sha256:1684133274a44010e6d6011d6eec100cf756309678c77700779ac44a5ac36715, and its linux/amd64 manifest digest is sha256:14671502e8e7b000cd644c1ad56934bab4a82077457af6461d084fd5d0e85c05. HF Sandbox cannot execute Docker digest references reliably, so runs must attest that the release tag still resolves to those digests before launch.

Do not place credentials in task files, generated images, command arguments, logs, or run manifests.

Findings

Agents submit one strict JSON document. See the findings format.

The verifier writes raw expected, generated, line-match, and semantic-match counts plus per-task metrics. The dataset-level metric.py sums counts across tasks before computing precision, recall, and F1, matching AACR-Bench's micro aggregation. Missing rewards and judge errors fail closed and suppress headline aggregate scores.

Model study status

The fixed five-task canary completed for Luna, Terra, and two DeepSeek/Pi configurations. Luna and Terra completed every trial. DeepSeek had 40% to 60% invalid trials and missed the registered replacement gates despite costing much less. It is not a viable replacement on the current evidence.

The frozen split named 59 sealed tasks. Four deleted Keycloak commits were recovered exactly from Software Heritage. Keycloak PR #35645 and Node.js PR #56185 were absent from checked public archives, leaving 57 released tasks and 462 references. The approved study contains 684 trials with an $80 hard ceiling. See the source corpus, canary results, and implementation plan.

Development

scripts/check.sh
uv run slophammer-py check . --execute
npx -y @simpledoc/simpledoc check
git diff --check

The implementation plan records the source baseline, hidden-verifier boundary, model-study protocol, and remaining publication work.

License

Apache-2.0