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Improving Counterexample-Guided Bidirectional Inductive Synthesis by an Incremental Approach
Yongho Yoon, Woosuk Lee, Kwangkeun Yi
http://doi.org/10.5626/JOK.2023.50.12.1091
One of the sources of inefficiency in counterexample-guided inductive synthesis algorithms is the fresh restart of inductive synthesis for each iteration. In this paper, we propose an incremental approach for the generalized counterexample-guided bidirectional inductive synthesis algorithm. The incremental algorithm reuses knowledge from the last iteration therefore reducing the search space, and making the remaining search faster. We applied our approach to the state-of-the-art bidirectional inductive synthesis algorithm, Simba, which is based on iterative forward-backward abstract interpretation. We implemented our approach and evaluated it on a set of benchmarks from the Simba paper. The experimental results showed that, on average, our approach reduces synthesis time to 74.2% of the original, without any loss in the quality.
Optimizing Homomorphic Compiler HedgeHog for DNN Model based on CKKS Homomorphic Encryption Scheme
Dongkwon Lee, Gyejin Lee, Suchan Kim, Woosung Song, Dohyung Lee, Hoon Kim, Seunghan Jo, Kyuyeon Park, Kwangkeun Yi
http://doi.org/10.5626/JOK.2022.49.9.743
We present a new state-of-the-art optimizing homomorphic compiler HedgeHog based on high-level input language. Although homomorphic encryption enables safe and secure third party computation, it is hard to build high-performance HE applications without expertise. Homomorphic compiler lowers this hurdle, but most of the existing compilers are based on HE scheme that does not support real number computation and a few compilers based on the CKKS HE scheme that supports real number computation uses low-level input language. We present an optimizing compiler HedgeHog whose input language supports high-level DNN operators. In addition to its ease of use, compiled HE code shows a maximum of 22% more of speedup than the existing state-of-the-art compiler.
Data-driven Path Selection for Improving Industrial-Strength Static Analyzers
http://doi.org/10.5626/JOK.2019.46.4.363
We propose a data-driven method to improve path-sensitive industrial-strength static analyzers. Most industrial static analyzers adopt path-sensitive techniques and path selection holds the key to their performance. We propose a method to automatically learn new cost-effective path-selection heuristics from an existing analyzer with a manually tuned path-selection heuristic. We evaluated our method on an industrial static C code bug-finder from Sparrow as a baseline analyzer with 17 C open-source benchmark programs. The experimental results showed that with the newly-learned path-selection heuristic, the analyzer reported 90.8% of the defects in only 38% of the analysis time, compared to the baseline analysis. This method reported more defects in less time than the baseline path-selection heuristic under similar path search space constraints.
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