SAS to Python Scalable Code Translation April 23, 2025 FedCASIC 2025 Reveal Global Consulting Cameron Milne, Ellie Mamantov, John Lynagh www.revealgc.com Agenda www.revealgc.com 2 3 Majority of surveys at the U.S. Census Bureau run in SAS
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Presentation Transcript
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SAS to Python
Scalable Code Translation April 23, 2025 FedCASIC 2025
Reveal Global Consulting
Cameron Milne, Ellie Mamantov, John Lynagh www.revealgc.com<br>
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Agenda www.revealgc.com 2<br>
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3 Majority of surveys at the U.S. Census Bureau run in SAS (developed in 1960s)
SAS licenses for enterprises are exceedingly expensive
Most universities prioritize Python and R for statistical programming
Census is migrating survey lifecycles to cloud environments Modernizing Systems at the Census Bureau www.revealgc.com<br>
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4 By-hand…
Rules-based approaches like Abstract Syntax Trees (AST) require hand-crafted rules and are inflexible and time consuming
Neural Machine Translation (NMT) requires massive amounts of parallel data for training which is unavailable for SAS and Python
Large Language Models (LLMs)? Options in Code Translation www.revealgc.com<br>
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www.revealgc.com 5 Sentinel Values (.M, .L, .N, etc.)
Operating over rows vs. vectorized operations
Merging nuances
Dataset size capabilities
Implied logic (e.g. mapping columns to matrices)
Modularized codebases add additional complexity Challenges unique to SAS<br>
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Design Constraints Goals
Outputs are identical
Strong “first-pass” translations
Minimal “last-mile” effort in testing
Preserve logic, comments, and more
Enable LLM to introduce Pythonic concepts 6 Constraints
Locally-hosted models
No feedback loops or access to data
Minimal redevelopment – stakeholders must map existing logic to new code
On-prem server environment (temporarily) www.revealgc.com<br>
Don’t give the model a chance to be lazy! www.revealgc.com<br>
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10 Chunking within Steps Smaller chunks will fit into the context window more easily, but how do you determine where to cut off? ~150 lines ~300 lines ~100 lines ~400 lines ~250 lines ~350 lines www.revealgc.com<br>
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Grouping for Token Optimization After chunking, we want to provide as much context as possible for the model
