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Synthesizing Piece-Wise Functions by Learning Classifiers

  • Daniel NeiderEmail author
  • Shambwaditya Saha
  • P. Madhusudan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9636)

Abstract

We present a novel general technique that uses classifier learning to synthesize piece-wise functions (functions that split the domain into regions and apply simpler functions to each region) against logical synthesis specifications. Our framework works by combining a synthesizer of functions for fixed concrete inputs and a synthesizer of predicates that can be used to define regions. We develop a theory of single-point refutable specifications that facilitate generating concrete counterexamples using constraint solvers. We implement the framework for synthesizing piece-wise functions in linear integer arithmetic, combining leaf expression synthesis using constraint-solving and predicate synthesis using enumeration, and tie them together using a decision tree classifier. We demonstrate that this approach is competitive compared to existing synthesis engines on a set of synthesis specifications.

Keywords

Synthesis Problem Constraint Solver Decision Tree Classifier Alternate Semantic Common Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was partially supported by NSF Expeditions in Computing ExCAPE Award #1138994.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Daniel Neider
    • 1
    • 2
    Email author
  • Shambwaditya Saha
    • 1
  • P. Madhusudan
    • 1
  1. 1.University of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.University of CaliforniaLos AngelesUSA

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