Tacas reproducibility
Skill brycewang-stanford/Awesome-Journal-Skills/TACAS-Skills/skills/tacas-reproducibility
Use when strengthening TACAS (ETAPS) reproducibility, covering the clean evaluation-VM packaging that the artifact process assumes, pinned dependencies and offline execution, a claim-to-script mapping so every benchmark number regenerates, honest degrees of reproducibility, consistency between the paper and the artifact, and the category difference between a mandatory tool-paper artifact and a voluntary research-paper artifact.From its SKILL.md
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill tacas-reproducibilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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TACAS Reproducibility
Use this before submission (for tool papers, before the mandatory artifact deadline) and again before camera-ready. At TACAS reproducibility is not a courtesy: for a regular tool or tool-demonstration paper the artifact is mandatory and feeds acceptance, and for a research or case-study paper a voluntary artifact earns the title-page badges. The goal is that an ETAPS evaluator, on a clean virtual machine, can rebuild your evidence and reach your numbers.
Design for the clean ETAPS VM
The artifact process assumes a provided VM image with bounded evaluator time. Package accordingly:
[Self-contained] ship a VM-ready package: a Dockerfile or a pinned environment (lockfile), with
the tool prebuilt; not "apt-get install 30 things and hope"
[Offline] no network access at run time; vendor every dependency, benchmark, and model
[Bounded] a short smoke run that finishes in minutes, plus a documented full run with its
expected (possibly long) runtime
[Deterministic] fix seeds and tool options; state what is and is not deterministic
[Documented] a README that orients an evaluator in one screen: what it is, how to run the
smoke test, how to reproduce each claim, expected outputs and runtimes
Claim-to-script mapping (the heart of a TACAS artifact)
- Map each reported table, figure, and headline number to a script and its expected output.
- Provide a top-level
reproduce/(or equivalent) that regenerates results from logged data, and a separate path that re-runs the tool from scratch for those who have the time budget. - State the machine you produced the numbers on; evaluators on different hardware should still see the same verdicts and the same relative comparison even if wall-clock differs.
Claim-to-evidence audit
| Claim in the paper | Weak artifact answer | TACAS-ready answer |
|---|---|---|
| "Solves N benchmarks" | "Benchmarks available on request" | The exact task set vendored + a script printing solved/unsolved |
| "Faster than <baseline>" | Only your tool shipped | Both tools (or the baseline's install) with the equal-budget harness |
| "Sound / sound up to k" | Verdicts unchecked | A validation script (witness checking / cross-tool agreement) |
| "The counterexample is real" | Prose only | A replayable witness the evaluator can validate |
"Available on request" and "works on our cluster" are treated as not reproducible at TACAS; convert every such line into a concrete, VM-runnable script.
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each table/figure from logged data on the VM.
- Scripted: scripts exist but need documented manual steps or a large external benchmark download.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For a tool paper, aim turnkey for the smoke run and the headline comparison — that is what the AEC checks against the Functional (and ideally Reusable) badge. Large full-benchmark runs may stay scripted with the time budget documented. Stating the achieved level honestly beats promising turnkey behaviour that fails on the clean VM.
Category difference
- Regular tool / tool-demonstration: the artifact is mandatory, submitted right after the paper, evaluated with the PC — plan it as a co-equal deliverable, not a follow-up.
- Regular research / case-study: the artifact is voluntary and post-acceptance; if you want the badges, prepare it after notification, but the paper must already be self-contained on the evidence side.
Consistency and camera-ready pass
- Before submission (or the artifact deadline): every scored number traces to a VM-runnable script; the paper and artifact agree; for a research paper the artifact is anonymized (no owner strings, cluster paths, lab names, identity-revealing repo URLs).
- Before camera-ready: swap any anonymized/placeholder location for a permanent, DOI-issuing archive
(Zenodo/figshare/Software Heritage) and align with the badges you earned (
tacas-artifact-evaluation).
Output format
[Claim inventory] <claim -> script -> expected output>
[Clean-VM readiness] runs offline on a fresh VM? smoke test in minutes? yes/no
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Soundness/validation] <witness or cross-tool check present? yes/no>
[Category obligation] mandatory (tool/tool-demo) / voluntary (research/case-study)
[Fixes before upload] <ordered list>
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.