ase-reproducibility

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?