EPISODE · Jan 16, 2018 · 14 MIN
Local Refinement Typing
from International Conference on Functional Programming 2017
Benjamin Cosman, University of California at San Diego, USA, gives the third talk in the second panel, Tools for Verification, on the 2nd day of the ICFP conference. Co-written by Ranjit Jhala, University of California at San Diego, USA. We introduce the FUSION algorithm for local refinement type inference, yielding a new SMT-based method for verifying programs with polymorphic data types and higher-order functions. FUSION is concise as the programmer need only write signatures for (externally exported) top-level functions and places with cyclic (recursive) dependencies, after which FUSION can predictably synthesize the most precise refinement types for all intermediate terms (expressible in the decidable refinement logic), thereby checking the program without false alarms. We have implemented FUSION and evaluated it on the benchmarks from the LiquidHaskell suite totalling about 12KLOC. FUSION checks an existing safety benchmark suite using about half as many templates as previously required and nearly 2x faster. In a new set of theorem proving benchmarks FUSION is both 10 - 50x faster and, by synthesizing the most precise types, avoids false alarms to make verification possible.
What this episode covers
Benjamin Cosman, University of California at San Diego, USA, gives the third talk in the second panel, Tools for Verification, on the 2nd day of the ICFP conference. Co-written by Ranjit Jhala, University of California at San Diego, USA. We introduce the FUSION algorithm for local refinement type inference, yielding a new SMT-based method for verifying programs with polymorphic data types and higher-order functions. FUSION is concise as the programmer need only write signatures for (externally exported) top-level functions and places with cyclic (recursive) dependencies, after which FUSION can predictably synthesize the most precise refinement types for all intermediate terms (expressible in the decidable refinement logic), thereby checking the program without false alarms. We have implemented FUSION and evaluated it on the benchmarks from the LiquidHaskell suite totalling about 12KLOC. FUSION checks an existing safety benchmark suite using about half as many templates as previously required and nearly 2x faster. In a new set of theorem proving benchmarks FUSION is both 10 - 50x faster and, by synthesizing the most precise types, avoids false alarms to make verification possible.
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Local Refinement Typing
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