调和枚举和演绎程序综合

Kangjing Huang, Xiaokang Qiu, Peiyuan Shen, Yanjun Wang
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引用次数: 30

摘要

语法引导合成(SyGuS)旨在找到满足语义规范和用户提供的结构假设的程序。有两种主要的综合方法:枚举综合,它反复枚举可能的候选程序并检查它们的正确性;演绎综合,它利用符号过程根据规范构造实现。这两种方法都不比另一种好:自动演绎合成通常非常有效,但只适用于特殊的语法或应用;枚举综合非常普遍适用,但可扩展性有限。在本文中,我们提出了一种基于条件线性整数算法(CLIA)背景理论的SyGuS问题的合作综合技术,作为两种方法的新颖集成,结合了两者的优点。该技术利用几种新的分治策略将大型综合问题分解为较小的子问题。子问题分别求解,它们的解结合起来形成最终解。该技术集成了两个合成引擎:可以有效解决某些问题的纯演绎组件和可以处理任意语法的基于高度的枚举算法。我们实现了协作合成技术,并在广泛的基准上对其进行了评估。实验表明,我们的技术可以解决许多以前不可能解决的具有挑战性的合成问题,并且往往比最先进的合成算法更具可扩展性。
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Reconciling enumerative and deductive program synthesis
Syntax-guided synthesis (SyGuS) aims to find a program satisfying semantic specification as well as user-provided structural hypotheses. There are two main synthesis approaches: enumerative synthesis, which repeatedly enumerates possible candidate programs and checks their correctness, and deductive synthesis, which leverages a symbolic procedure to construct implementations from specifications. Neither approach is strictly better than the other: automated deductive synthesis is usually very efficient but only works for special grammars or applications; enumerative synthesis is very generally applicable but limited in scalability. In this paper, we propose a cooperative synthesis technique for SyGuS problems with the conditional linear integer arithmetic (CLIA) background theory, as a novel integration of the two approaches, combining the best of the two worlds. The technique exploits several novel divide-and-conquer strategies to split a large synthesis problem to smaller subproblems. The subproblems are solved separately and their solutions are combined to form a final solution. The technique integrates two synthesis engines: a pure deductive component that can efficiently solve some problems, and a height-based enumeration algorithm that can handle arbitrary grammar. We implemented the cooperative synthesis technique, and evaluated it on a wide range of benchmarks. Experiments showed that our technique can solve many challenging synthesis problems not possible before, and tends to be more scalable than state-of-the-art synthesis algorithms.
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