Software Open Access
Padhi, Saswat; Millstein, Todd; Nori, Aditya; Sharma, Rahul
In syntax-guided synthesis (SyGuS), a synthesizer's goal is to automatically generate a program belonging to a grammar of possible implementations that meets a logical specification. We investigate a common limitation across state-of-the-art SyGuS tools that perform counterexample-guided inductive synthesis (CEGIS). We empirically observe that as the expressiveness of the provided grammar increases, the performance of these tools degrades significantly.
We claim that this degradation is not only due to a larger search space, but also due to overfitting. We formally define this phenomenon and prove no-free-lunch theorems for SyGuS, which reveal a fundamental tradeoff between synthesizer performance and grammar expressiveness.
A standard approach to mitigate overfitting in machine learning is to run multiple learners with varying expressiveness in parallel. We demonstrate that this insight can immediately benefit existing SyGuS tools. We also propose a novel single-threaded technique called hybrid enumeration that interleaves different grammars and outperforms the winner of the 2018 SyGuS competition (Inv track), solving more problems and achieving a 5x mean speedup.
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This software artifact contains all 180 invariant-inference SyGuS benchmarks, implementations of all 6 grammars presented in our paper, working code for the hybrid enumeration (
HEnum) technique, and several scripts to reproduce the empirical claims made in the paper:
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sha1sumof the zip file matches with
cav19-artifact-100.sha1files to the same directory.
sha1sum -c cav19-artifact-100.sha1.
virtualboxon your machine.
.ovaimage to your virtualbox.
CTRL+ALT+T) and try:
make clean ; make dependencies
Everything built!message, then you can proceed with the artifact evaluation steps.