CoNEXT Artifact Evaluation

SkillDev tools

Use when converting an accepted ACM CoNEXT paper's package into optional ACM reproducibility badges via the CoNEXT reproducibility committee, from the badge opt-in due before submission, to the one-page artifact description due within a week of acceptance, to the Available / Functional / Reusable / Reproduced criteria and evaluator-proof reuse docs.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the CoNEXT Artifact Evaluation skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in CoNEXT-Skills/skills/conext-artifact-evaluation/SKILL.md and read by ahel’s review.

Turn the accepted paper's package into optional ACM reproducibility badges, administered by CoNEXT's reproducibility committee. This is a distinct process from the paper review, with its own timing: the decisive move — opting in before the paper submission deadline — happens long before you have an accept. Treat badging as a track you enter at submission and complete after acceptance.

The badge timeline (opt-in is the gate)

[Before submission deadline]  OPT IN for ACM badging  <-- miss this and you are ineligible this cycle
[Within ~1 week of accept]    send a ONE-PAGE artifact description + pointers to the committee
[Post-accept evaluation]      committee reviews the artifact against the ACM badge criteria
[Camera-ready]                approved badges appear on the PACMNET article (see conext-camera-ready)

The ACM badges (what each requires)

CoNEXT uses the ACM Artifact Review and Badging scheme. In practice the ladder is:

BadgeWhat it certifiesWhat you must provide
Artifacts AvailableThe artifact is publicly archivedA DOI-issuing archive (Zenodo/figshare/Software Heritage) with an open license
Artifacts Evaluated — FunctionalIt is documented, complete, and runsClear docs, a runnable package, and a "getting started" path that works
Artifacts Evaluated — ReusableIt is functional and others can build on itReusability docs, clean structure, parameterization, and a license that permits reuse
Results ReproducedKey results were regenerated by evaluatorsA scripted path that regenerates the paper's central figures/tables from the inputs

Confirm the exact badge set and names offered by the current CoNEXT reproducibility committee — the scheme is stable but wording is cycle-volatile (待核实).

The one-page artifact description

The committee starts from your one-pager, so make it do work:

  • Inventory: code, traces, configs, testbed scripts — with a pointer to each.
  • Requirements: the hardware/software an evaluator needs. Networking artifacts often need specific hardware (a particular switch ASIC, NIC, or SDR); if evaluators need access to hardware you must provide, flag it prominently and describe how you will give access.
  • Claims supported: which paper results the artifact reproduces, and which it cannot (be honest about hardware-bound results).
  • Getting started: the shortest path to a first successful run.

Networking-specific evaluation hurdles

  • Hardware dependence. A result that needs a specific switch or NIC is hard for a remote evaluator; provide a testbed reservation, a remote-access path, or a faithful emulation for the parts that can run in software, and clearly mark what is hardware-bound.
  • Trace size and privacy. Large or sensitive traces may not be fully releasable; ship a representative or synthetic subset plus the pipeline, and document the restriction.
  • Environment drift. Pin firmware/OS/kernel versions and provide images; "works on our testbed" is not reproducible.
  • Live dependencies. Cache anything that would otherwise require a live API/network call so the evaluator's run is deterministic.

Reusability that survives an evaluator

  • A README that assumes nothing about your lab's setup; every path and dependency explicit.
  • A single command (or a short script) for the "getting started" run, and a documented full-reproduction path for the central results.
  • An open license present and consistent across the archive.
  • A mapping from paper claim → artifact step → expected output, so an evaluator can check "Results Reproduced" without guessing.

Pre-evaluation checklist

[Opt-in]        confirmed before the submission deadline? yes/no
[Archive]       DOI-issuing archive + open license? yes/no
[One-pager]     inventory + requirements + hardware-access plan + claims map? yes/no
[Functional]    fresh-environment run succeeds from the docs alone? yes/no
[Reusable]      structure + docs let a third party adapt it? yes/no
[Reproduced]    central figures/tables regenerate by script? yes/no
[Hardware]      hardware-bound results flagged; access path provided? yes/no

Output format

[Badge target] Available / Functional / Reusable / Reproduced (which are in scope)
[Opt-in status] done before submission? yes/no
[Artifact readiness] <per-badge gaps>
[Hardware plan] <how remote evaluators reach any required hardware>
[One-pager] <drafted / sent to committee>

Signals

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skill
Key
conext-artifact-evaluation
Source
github.com/brycewang-stanford/awesome-journal-skills