POPL Experiments & Evidence
Guides the design, proof effort accounting, and empirical evaluation reporting for POPL research papers.
POPL Experiments helps programming language researchers design and structure the empirical components of their POPL conference papers. It ensures empirical evidence appropriately matches formal theoretical claims, standardizes proof effort accounting across proof assistants like Rocq/Coq and Lean, provides rigorous case-study selection guidelines, and enforces honest reporting of limitations, failures, and reproducible benchmarks so the paper's formal contribution remains central.
Key Features
01Standardizes proof effort metrics for Lean and Rocq/Coq developments
02Enforces case-study diversity and structured bad-news/failure reporting
03Aligns performance measurement and benchmarking with POPL artifact standards
04Maps formal theoretical claims directly to appropriate empirical evidence types
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06Generates structured audit summaries covering claim mapping and regenerability
Use Cases
01Measuring and formatting proof effort lines of code and lemma reuse for interactive theorem provers
02Structuring the evaluation and case-study section of a programming languages paper
03Auditing draft experiment sections against POPL reviewer standards before submission