aaai-artifact-evaluati…
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance…
Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting,
Claude Code
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments --agent claude-code
Codex
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a codex -y
Cursor
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill atc-experiments -a windsurf -y
name: atc-experiments description: Use when designing or auditing the evaluation of an ATC (ACM SIGOPS Annual Technical Conference, formerly USENIX ATC) systems paper — matching evidence to the claim with real testbeds, fair baselines, end-to-end plus microbenchmark results, tail-latency and variance reporting, workload realism, and honest cost accounting.
Match the evidence to the claim. ATC is the systems community's **implementation-and-measurement** venue: reviewers read for measured behavior on a real system, not asymptotics or accuracy on a dataset. In round two, 3-4 reviewers close to your subarea will open the artifact and probe whether the numbers are end-to-end, fair, and honest about cost. Design the evaluation so their first three objections are already answered.
| If your claim is... | The evidence ATC expects | |---|---| | "Faster / lower latency" | End-to-end latency **including tails (p99/p999)** and throughput at a **matched** operating point, on a described testbed | | "Lower overhead / cheaper" | The resource cost (CPU, memory, writes, energy) measured, at matched function — not just the headline win | | "Scales" | Measurements across a real range of load/nodes/cores with the scaling curve and where it bends | | "More reliable / correct" | Fault-injection or crash/recovery experiments, not just steady-state runs | | "Useful in practice" (experience) | Production-derived workloads and lessons; what broke and what generalizes |
kernel/OS versions, network topology, and any co-location. A result without its testbed is not a systems result.
generators beat hand-picked inputs. State how the workload was obtained and why it is representative; if it is synthetic, justify the parameters.
duration — systems reviewers know where transient effects hide.
caught immediately). If you tuned your system, tune the baseline.
same load. An unmatched comparison is the classic systems-reviewer objection.
as the reference.
ATC reviewers want both:
than to unrelated system effects. A paper with only end-to-end numbers cannot explain *why*; one with only microbenchmarks cannot show it *matters*.
point, not a result.
win with an unstated cost reads as a hidden weakness.
nor hurts" builds more trust than a uniformly rosy curve.
Pin these at collection time — they cannot be recovered at the deadline (see `atc-reproducibility`):
[Hardware] CPU/NIC/SSD models, core/memory counts, firmware where it matters [Software] kernel/OS versions, library and compiler versions, config flags [Workload] trace source and date, generator version and seeds, request mix [Method] warm-up window, measurement duration, number of runs, aggregation [Code] commit SHAs for the system and every baseline
a lucky run.
cover.
transferable lessons; ATC's Deployed Systems lane values this even without a new mechanism.
[Claim -> evidence] each claim mapped to the experiment that supports it; gaps flagged [Testbed] hardware/software/workload described enough to reproduce? yes/no [Baselines] strongest alternative, well-configured, at a matched operating point? yes/no [Depth] end-to-end AND microbenchmarks present? tails + variance reported? [Honesty] costs reported beside gains? neutral/negative regions stated? [Provenance] hardware/software/workload/method/code pinned at collection time? yes/no
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