OOPSLA Empirical Reproducibility
Hardens empirical claims and benchmark evaluations in OOPSLA papers against SIGPLAN Empirical Evaluation Guidelines.
OOPSLA Empirical Reproducibility equips programming language researchers with a rigorous framework to audit, structure, and validate empirical evaluations for OOPSLA submissions according to SIGPLAN guidelines. It systematically catches critical managed-runtime pitfalls—including JIT warmup artifacts, GC scheduling variance, and skewed summary statistics—while generating machine-readable reproducibility ledgers and verifiable Data-Availability Statements. Useful for authors preparing initial submissions or revision rounds, this skill helps preempt reviewer skepticism and ensures seamless artifact evaluation badge compliance.
主な機能
01Generates machine-readable YAML reproducibility ledgers for experiment pinning
02Identifies managed-runtime traps like JIT warmup and compilation variance
03Conducts pre-submission self-audits to preempt minor and major reviewer revisions
04Drafts verifiable Data-Availability Statements aligned with artifact requirements
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06Audits empirical claims across the four SIGPLAN evaluation pillars
ユースケース
01Drafting accurate Data-Availability Statements and experiment ledgers for artifact evaluation
02Pre-submission self-auditing of performance claims, benchmark suites, and statistical metrics
03Addressing reviewer revision demands regarding baseline fairness and runtime variance