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Use when preparing an accepted ASE (IEEE/ACM Automated Software Engineering) paper's tool and data for the Artifact Evaluation track, targeting the ACM Artifacts Available and Artifacts Reusable badges on the track's own deadline, with the badge shown on the paper's front page in both IEEE Xplore and the ACM Digital Library.
Passed 21 of 22 test cases with the skill, and 16 of 22 without it.
and 11 more in the full report
6 fixed15 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Can you check our artifact checklist before submission? Organize your assessment using the required six bracketed key format.
| Checked | Without skill | With skill |
|---|---|---|
| The response presents all six bracketed key sections. | Missed | Passed |
| The response lists blockers in an ordered priority list. | Missed | Passed |
The instructions your model reads when the skill loads.
Convert the accepted paper's package into badges. ASE runs an Artifact Evaluation track offering the Artifacts Available and Artifacts Reusable badges (ACM scheme). Because ASE proceedings are indexed in both IEEE Xplore and the ACM Digital Library, an earned badge appears on the paper's front page in both. Evaluation happens on the track's own deadline, separate from the research-track notification — stage the package before then.
The review-time (anonymized) artifact and the badge artifact are the same package matured. After
acceptance you can de-anonymize it, but the substance should already be there if you followed
ase-reproducibility.
[De-anonymize] restore the real tool name, authors, repository, license.
[Archive] deposit in a DOI-issuing archive; the DOI is what "Available" certifies.
[Document] README with exact run path, expected outputs, and a small worked example.
[Environment] container/lockfile pinning deps + the exact tool commit; note hardware needs.
[Reuse story] show how to run the tool on a NEW input, not just replay your experiments.
Evaluators judge reusability, so write for someone who wants to use your automation on their own code:
test cases passed, vs 16 / 22 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.