fse-reproducibility
GitHub用于强化软件工程顶会ESEC/FSE的可重复性与开放科学证据。涵盖数据可用性声明审计、证据映射、版本固定及可重复性等级声明，确保论文与工件一致，满足评审要求。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility -g -y
SKILL.md
Frontmatter
{
"name": "fse-reproducibility",
"description": "Use when strengthening ESEC\/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for mining and LLM studies, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper says and what the artifact contains."
}
FSE Reproducibility
Use this before submission and again before camera-ready. FSE's open-science posture makes reproducibility a scored dimension, not a courtesy: the double-anonymous review already expects an inspectable artifact, and the PACMSE camera-ready expects a permanent one. The goal is that a competent reader could rebuild your evidence and reach your conclusions.
Evidence map
- Map each research-question answer, technique claim, and reported number to a verifiable location — a paper section, a table generated from logged data, or a script in the artifact.
- For techniques, give enough of the algorithm, parameters, and environment that a reader could re-implement or re-run it.
- For empirical studies, report subjects and their selection, data collection, preprocessing, metrics, statistics, and the analysis scripts.
- Keep the Data Availability statement truthful and specific: what is shared, where it will live after acceptance, and — if something cannot be shared — exactly why.
- Keep the paper and the artifact consistent: a number in the PDF that no script in the artifact produces is the contradiction reviewers read as carelessness.
Data Availability statement audit
| Claim in the paper | Weak availability answer | FSE-ready answer |
|---|---|---|
| "We study N projects" | "Dataset available on request" | Anonymized archive of the exact project list + extraction scripts |
| "Our tool detects X" | "Code will be released" | Anonymized, runnable tool with a README and a small demo input |
| "We interviewed P developers" | Nothing (privacy cited vaguely) | Anonymized codebook, protocol, and aggregate data; stated ethics limits |
| "The model produced Y" | Live API described | Cached prompts and raw responses, model IDs and dates |
"Available on request" is treated as not available at FSE; convert every such line into a concrete, anonymized artifact or an explicit, justified exception.
Provenance pinning
[Mining] pin repository SHAs; record corpus extraction date; archive the extracted dataset,
not just the query; document fork/duplicate/bot handling
[LLM] record exact model identifiers + access dates; cache raw inputs and outputs; report
sampling settings; prefer post-training-cutoff subjects to bound contamination
[Compute] state hardware, runtime, and number of runs so a reader can size a reproduction
[Randomness] log seeds for any stochastic step; say what is and is not deterministic
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each table/figure from logged data.
- Scripted: scripts exist but require documented manual steps or external data access.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For FSE, aim turnkey for anything a reviewer might rerun quickly (a detection script on sample inputs, a plot from logged results); large mined corpora or industrial data may stay scripted with access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Vignette: a mixed-methods study
Consider a study combining mined pull-request data with a developer survey. Its reproducibility spine: the mining scripts with pinned SHAs and extraction date; the anonymized extracted dataset; the survey instrument and anonymized responses; the qualitative codebook with inter-rater agreement; and the analysis notebooks that turn all of it into the paper's tables — plus one honest sentence about the parts (raw identities, private repositories) that cannot be shared and why.
Consistency and camera-ready pass
- Before submission: every scored number traces to the artifact; the Data Availability statement matches reality; the artifact is anonymized (no owner strings, cluster paths, or lab names).
- Before camera-ready: swap anonymized links for permanent, DOI-issuing archives, and align the
statement with the ACM artifact badges you are pursuing (
fse-artifact-evaluation).
Output format
[Claim inventory] <claim -> evidence location>
[Data Availability] concrete / vague / missing
[Provenance gaps] <mining SHAs / LLM caching / seeds / compute>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload>
Version History
- 9f86f09 Current 2026-07-19 15:45
