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name: ase-reproducibility description: Use when building the open-science and reproducibility story for an ASE (IEEE/ACM Automated Software Engineering) submission, covering the mandatory Data Availability Statement, anonymized-but-runnable tools, tool and subject-system provenance pinning, cached LLM outputs, and staging for the ACM Available/Reusable artifact badges.

ASE Reproducibility

Build the reproducibility story at data-collection time, not at submission. ASE requires a mandatory Data Availability Statement in the paper and expects an anonymized, runnable artifact at review time; automated-SE artifacts are usually tools, so "runnable" means a reviewer can actually execute the automation on stated subjects. What is not pinned when you collect it cannot be reconstructed later.

The mandatory Data Availability Statement

• Required, placed after the Conclusions and inside the 10-page limit (it is not free appendix space).
• State what exists — the tool, the dataset, the subject systems, the scripts, the logs — and where it will live after acceptance (an archival DOI target).
• Provide an anonymized link or upload now; "available upon request" reads as a scored weakness, not a neutral placeholder.
• Match the statement to what the archive actually contains — an overclaiming statement is worse than a modest, honest one.

Anonymized-but-runnable tools

• Re-host the tool and dataset behind an anonymizing service; strip repository owner, commit author metadata, and any path revealing your identity (/home/<you>/, institutional URLs).
• Include a minimal run path: exact commands, expected inputs, and a small sample so a reviewer can execute the automation without your machine.
• Pin the environment: dependencies with versions, a container or lockfile, and the exact tool commit — automated-SE tools rot fast against moving toolchains.

Provenance pinning (do this at collection time)

For the tool:

• Exact commit SHA, build instructions, dependency versions, and configuration/flags used in the experiments (including seeds for randomized components).

For subject systems and datasets:

• Names, versions, and SHAs of every subject; the corpus extraction date; query/filter criteria; and any manual labeling protocol with inter-rater agreement.
• A regeneration script and a versioned snapshot — live scraping re-samples a moving target.

For LLM-based components:

• Model identifiers and dates, prompts, decoding settings, and cached raw outputs so the artifact reproduces rather than calls a live, drifting API.

Reproducibility failure modes (ASE-specific)

Failure	Consequence	Prevention
Tool needs your exact machine	Reviewers cannot run it; artifact fails	Container/lockfile + minimal run path
Subjects unpinned (branch, not SHA)	Numbers cannot be reproduced	Record SHAs + extraction date at collection
LLM outputs uncached	Re-runs drift; comparison invalid	Cache outputs; record model IDs/dates
Data Availability outside the 10 pages	Policy violation	Place it after Conclusions, inside the budget
Identity leak in artifact	Anonymity violation	Scrub owner/metadata; re-host anonymized

From submission to the ACM badges

The submission-time artifact and the post-acceptance badge artifact are the same package matured. ASE offers Artifacts Available and Artifacts Reusable badges (ACM scheme); staging for them now avoids a scramble later (see ase-artifact-evaluation):

• Available — deposit in a DOI-issuing archive (Zenodo / figshare / Software Heritage) with an open license.
• Reusable — documentation, a clear run path, and structure that lets a stranger reuse the tool beyond reproducing your tables.

Output format

[Data Availability] present, after Conclusions, inside 10pp? matches the archive?
[Tool] commit pinned, deps versioned, container/lockfile, minimal run path?
[Subjects/data] SHAs + extraction date + selection/labeling protocol recorded?
[LLM] model IDs/dates, prompts, cached outputs?
[Anonymity] owner/metadata scrubbed; anonymized re-host?
[Badge readiness] Available (DOI+license) / Reusable (docs+run path) staged?