Fse reproducibility

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>