edbt-reproducibility
GitHub用于增强EDBT数据库系统论文的可复现性，涵盖可运行工件、环境锁定、数据溯源及声明审计。确保论文与开源包一致，提供从描述性到开箱即用的可复现性分级指导，提升论文可信度。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill edbt-reproducibility -g -y
SKILL.md
Frontmatter
{
"name": "edbt-reproducibility",
"description": "Use when strengthening EDBT reproducibility for a database-systems paper, covering a runnable artifact, pinned environments and workloads, dataset and query-log provenance, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between the paper and the package for the open-access OpenProceedings record."
}
EDBT Reproducibility
Use this before submission and again before camera-ready. EDBT's community has a reproducibility-forward culture, and the published record is open access on OpenProceedings — so an inspectable, re-runnable package raises a paper's standing and, for an Experiments & Analysis paper, is the contribution. The goal is that a competent reader could rebuild your measurements and reach your conclusions.
Evidence map
- Map each claim, mechanism, and reported number to a verifiable location — a paper section, a table generated from a logged run, or a script in the artifact.
- For a mechanism, give enough of the algorithm, data structures, parameters, and system integration that a reader could reimplement or rebuild it.
- For an evaluation, report workloads and their derivation, dataset versions and sources, the measurement harness, metrics, and the analysis scripts.
- Keep the availability statement truthful and specific: what is shared, the workloads and data, the hardware assumptions, and — if something cannot be shared — exactly why.
- Keep the paper and the artifact consistent: a number in the PDF that no script produces is the contradiction reviewers read as carelessness.
Availability statement audit
| Claim in the paper | Weak availability answer | EDBT-ready answer |
|---|---|---|
| "We evaluate on workload W" | "Data available on request" | Archived workload/query-log derivation + the extracted data or a documented access path |
| "Our operator lowers latency" | "Code will be released" | Runnable system/prototype with a build, a demo run, and the config |
| "We compare N systems" (E&A) | Numbers with no harness | The full comparison harness that regenerates every table |
| "On a 128-node cluster" | Nothing about environment | Hardware/cluster spec, engine build/commit, and how to size a smaller reproduction |
"Available on request" is treated as not available; convert every such line into a concrete package or an explicit, justified exception (licensing, confidentiality).
Provenance pinning (database-systems flavor)
[Data] pin dataset versions and sources; archive the derived workload/query-log, not just a
description; document filtering and sampling
[System] record the engine/prototype build or commit; ship a build recipe or container
[Environment] state hardware, memory, network, and node counts; note what a smaller reproduction changes
[Harness] the measurement scripts that produce each table/figure, with fixed configuration
[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 (or container) regenerates each table/figure from a run or from logged results.
- Scripted: scripts exist but require documented manual steps, a specific cluster, or external data access.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For EDBT, aim turnkey for anything a reviewer might re-run quickly (a demo run on a small workload, a plot from logged results); large-cluster or licensed-data experiments may stay scripted with the environment and access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Vignette: a distributed-operator study
Consider an operator evaluated on a cluster. Its reproducibility spine: a container or build recipe for the engine plus the operator; the workload-derivation scripts with pinned dataset versions; the measurement harness that runs the operator and the tuned baseline across node counts; the logged raw results; and the analysis notebooks that turn them into the paper's tables — plus one honest sentence about the parts (the full 128-node run, a licensed dataset) that a reader reproduces at reduced scale and why.
Consistency and camera-ready pass
- Before submission: every reported number traces to the artifact; the availability statement matches reality; if the cycle is double-blind, the artifact carries no identity strings.
- Before camera-ready: deposit the package in a DOI-issuing archive (Zenodo, figshare, Software
Heritage) with an OSI-approved license, replace any anonymized links with the permanent ones, and
make the statement consistent with the open-access OpenProceedings record
(
edbt-artifact-evaluation,edbt-camera-ready).
Output format
[Claim inventory] <claim -> evidence location>
[Availability] concrete / vague / missing
[Provenance gaps] <dataset versions / engine build / environment / seeds>
[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 15:16
