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Icde artifact evaluation

Skill brycewang-stanford/Awesome-Journal-Skills/ICDE-Skills/skills/icde-artifact-evaluation

Use when packaging IEEE ICDE code, data, workload generators, and logs as supplemental material whose availability reviewers score, and for any post-acceptance reproducibility or badge process the edition runs. Covers what a builder-heavy committee inspects first, making a data-systems benchmark turnkey, and single-blind packaging.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill icde-artifact-evaluation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

4.1 KB, 816 tokens by cl100k_base, as published. Nobody here has run it

ICDE Artifact Evaluation

Use this for evidence packaging around ICDE. ICDE expects authors to submit supplemental material and considers its availability in the evaluation, so a strong artifact directly raises the paper's floor even where no separate badge process runs. Confirm whether the current edition also runs a post-acceptance reproducibility/badge track (待核实) before promising evaluators anything.

Artifact plan

  • Decide what evidence a builder needs to believe the numbers: the system source, the workload generators, the baselines' configurations, the datasets or their construction scripts, seeds, and logs.
  • Keep decision-critical evidence in the paper or its figures; the artifact demonstrates reproducibility, it does not replace the argument.
  • Provide a minimal reproduction map: build steps, dependencies, hardware assumptions (especially the storage device), commands, expected outputs, runtime, and known nondeterminism sources.
  • For restricted or proprietary data, give enough provenance and construction detail for a credible re-run without violating data-use terms.
  • After acceptance, publish the public, licensed, tagged version whose commit produced the paper's numbers.

What ICDE evidence reviewers open first

Claim typeFirst artifact inspectedCommon failure caught
Throughput/latency winrun_small.sh and the workload generatorNumbers cannot be regenerated; generator or seeds absent
"Mechanism causes the gain"The ablation toggle in the codeThe toggle does not exist; gain not isolable in the artifact
Baseline comparisonThe baseline's config filesBaseline was untuned or run with defaults
Scale claimThe scale-factor sweep scriptOnly one scale factor is actually runnable
Cost/overhead claimThe instrumentation that measures costCost is asserted but not measured anywhere

Because ICDE reviewers are builders, they will re-run a small benchmark far sooner than they will provision a cluster — make run_small.sh reproduce the headline crossover on one machine in minutes before polishing anything else.

Worked vignette: packaging a storage-engine benchmark

A submission proposes a write-optimized index validated on a telemetry trace and a synthetic sweep.

  • Ship the workload as a parameterized generator (append rate, append-to-scan ratio, key distribution), not constants buried in a driver, so reviewers can vary the regime.
  • Record the exact seed sequence and run count behind every throughput and latency-tail figure; percentile claims are meaningless without them.
  • Emit figures directly from logged runs so PDF and artifact numbers cannot drift.
  • Include the ablation switch and the baseline configs so a reviewer can reproduce both the effect and the fair comparison.

Single-blind and logistics anchors

  • ICDE is single-blind: the artifact need not be anonymized — leave author names and history in place; spend the effort on making it build and run.
  • Assume, absent a formal badge track, that only the README and one entry script get opened; design for that. If a badge/reproducibility process does run this edition, read its criteria before packaging.
  • Upload size limits and accepted formats vary by edition; verify against the current CMT submission form.

Output format

[Artifact role] scored supplement / post-acceptance reproducibility / public archive
[Contents] <source / generators / baseline-configs / logs / claims-map>
[Turnkey check] <does run_small.sh reproduce the headline result? y/n>
[Isolability] <is the mechanism ablation runnable in the artifact? y/n>
[Hygiene] <secrets / caches / bloat removed>
[Fixes before upload] <ordered list>

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.