edbt-reproducibility

GitHub

指导数据库系统论文提升可复现性,涵盖构建可运行工件、固定环境与数据版本、建立声明与证据映射、审计可用性说明及记录复现程度,确保论文与开源包一致。

EDBT-Skills/skills/edbt-reproducibility/SKILL.md brycewang-stanford/Awesome-Journal-Skills

Trigger Scenarios

准备提交 EDBT 数据库会议论文 整理实验分析与系统实现的开源工件 审计论文的可用性声明与数据来源

Install

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill edbt-reproducibility -g -y
More Options

Non-standard path

npx skills add https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/EDBT-Skills/skills/edbt-reproducibility -g -y

Use without installing

npx skills use brycewang-stanford/Awesome-Journal-Skills@edbt-reproducibility

指定 Agent (Claude Code)

npx skills add brycewang-stanford/Awesome-Journal-Skills --skill edbt-reproducibility -a claude-code -g -y

安装 repo 全部 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --all -g -y

预览 repo 内 skill

npx skills add brycewang-stanford/Awesome-Journal-Skills --list

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

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