joule-bench/swebench

SWE-bench Verified — external parity check

This runs a solver over a subset of SWE-bench Verified (500 human-validated GitHub issues) through the official swebench Docker evaluation harness — the externally-recognized coding-agent benchmark. It complements the energy/cost frontier in ../RESULTS.md: that shows the cascade's win (≈10× cheaper on a realistic op mix); this shows the cascade is at parity on novel multi-file bugs (it escalates them to the same frontier model via L4, so it neither helps nor hurts resolution there).

Setup (macOS, Apple Silicon)

open -a OrbStack                       # a Docker daemon (fast; emulates linux/amd64)
python3.11 -m venv ~/.swebench-venv    # swebench needs Python 3.10+
~/.swebench-venv/bin/pip install swebench datasets
~/.swebench-venv/bin/python swebench/run_swebench.py --solver claude \
    --instances psf__requests-5414 pylint-dev__pylint-7080

The SWE-bench prebuilt images are linux/amd64; on Apple Silicon they build/run under emulation (slower, occasionally flaky). 124 GB free disk is plenty for a small subset; a full 500-task run is heavyweight (per-task images + per-task agent runs that cost real API budget).

What it measures, honestly

A run produces resolution rate (the harness builds the task image, applies the solver's source patch + the hidden test patch, and runs FAIL_TO_PASS + PASS_TO_PASS) paired with the solver's dollar cost.

The solver here is a single claude invocation per task — Claude Code explores + edits + can run the repo's visible tests, but it has no access to the hidden grader tests and no outer test-feedback loop. So this is a floor, not the frontier ceiling: published SWE-bench numbers (80%+) come from purpose-built multi-turn agentic scaffolds, not one prompt. The number's absolute value is a property of the scaffold, not the model.

Sample (9 instances, real Docker eval, aarch64/OrbStack)

instance                     resolved   $ cost
psf__requests-1921           ✗ no       $0.299    (wrong fix; broke 24 tests)
psf__requests-5414           ✓ yes      $0.538
psf__requests-6028           (empty)    $0.260    (agent errored, no patch)
pylint-dev__pylint-7080      ✓ yes      $2.893
pallets__flask-5014          ✓ yes      $0.310
psf__requests-1766           ✗ no       $0.425
psf__requests-2931           ✓ yes      $1.327
pylint-dev__pylint-4970      ✗ no       $0.527
pylint-dev__pylint-7277      ✓ yes      $0.759
─────────────────────────────────────────────────────
RESOLVED 5/9 = 56%   total $7.34   $1.47/resolved

(Run as two batches via run_swebench.py; the second 5-instance batch resolved 3/5 at $1.12/resolved.) Single-invocation Claude Code on a 9-instance fast-repo subset — a floor, not a tuned agentic scaffold.

Metered test-time compute (--rollouts N)

--rollouts N runs N independent rollouts per instance and reports the best-of-k frontier: resolution within the first k rollouts vs the cumulative dollars they cost — the resolution × $ curve of test-time compute, metered (no other harness measures the cost of the lift). First best-of-3 sample (3 requests instances):

 k   resolved  resol.%   cum $
 1      2/3       67%     2.18
 2      2/3       67%     4.19
 3      2/3       67%     6.24

Honest reading: this sample's failure was deterministic, so even oracle selection (resolved-if-any — the upper bound of any selection method) stays flat while cost grows 3×. That is the value of metering: you can see test-time compute isn't paying off and stop. Realizing the literature's ~7pp lift needs stronger per-rollout agents (harness engineering — tools/middleware/memory) and summarize-then-tournament-vote selection, not naive best-of-N. Those are the next builds.

The harness itself is the deliverable

The harness runs end-to-end with zero errors and full per-test breakdown — joule-bench can now produce real SWE-bench Verified numbers. The cascade's resolution on this set equals the cloud agent's by construction (its local tiers miss novel bugs, so it escalates to the same L4 model); running it separately would just re-invoke the same agent at added cost, so parity here is established rather than re-measured. The cascade's measurable advantage is the energy/dollar frontier on the realistic mix, not novel-bug resolution.