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pldi-experiments

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Use when designing or auditing a PLDI evaluation — choosing defensible benchmark suites and baseline compiler configurations, measuring runtime, compile time, and memory with warmup and variance discipline, running ablations that isolate the claimed mechanism, and scoping claims to the platforms measured.

PLDI-Skills/skills/pldi-experiments/SKILL.md

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# PLDI Experiments

A PLDI evaluation answers one question: *does the claimed mechanism cause the
claimed effect on programs that matter?* Everything in the design flows from
making that causal link auditable. The community's shared rubric is SIGPLAN's
Empirical Evaluation checklist (see `pldi-reproducibility` for the measurement
hygiene); this skill covers the design choices above the hygiene layer.

## Benchmark choice is an argument, not a default

- **Justify the suite** relative to the claim: an allocation optimizer needs
  allocation-heavy programs *and* allocation-light ones (to show no regression);
  a parser-facing analysis needs real grammars, not microbenchmarks.
- **Use community suites where they exist** and state versions; add real-world
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