OOPSLA Experiment Designer
Designs and audits experimental evaluations for OOPSLA academic papers to align evidence with claims and meet SIGPLAN guidelines.
OOPSLA Experiment Designer guides computer science researchers in constructing rigorous, peer-review-ready evaluation sections tailored for OOPSLA and related SIGPLAN conferences. It maps specific research claims to their required empirical evidence types—such as benchmarks, corpus studies, case studies, or mechanized proofs—while auditing baseline selections, workload rules, and statistical rigor. By anticipating reviewer demands and helping structure experimental matrices ahead of review rounds, this skill minimizes the risk of major revisions and accelerates the path to publication.
주요 기능
01Maps paper claims directly to proper empirical evidence types (benchmarks, corpus studies, user studies)
02Generates structured audit reports covering routing, baselines, workload rules, and analysis compliance
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04Audits baseline configurations and workload selection rules against SIGPLAN standards
05Prepares anticipated reviewer demand matrices sized for two-round conference resubmission cycles
06Enforces statistical floors including repetition counts, dispersion metrics (CI/IQR), and appropriate summary metrics
사용 사례
01Structuring resubmission experimental matrices to quickly resolve Major Revision feedback between review rounds
02Auditing an existing paper draft's evaluation section against common SIGPLAN reviewer rejection points
03Planning experimental methodology and baseline selection before submitting an OOPSLA research paper