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?
