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.
Thu 21 OctDisplayed time zone: Central Time (US & Canada) change
15:40 - 17:00 | |||
15:40 15mTalk | A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML) SIGPLAN Papers Muhammad Usman University of Texas at Austin, USA, Wenxi Wang University of Texas at Austin, Marko Vasic University of Texas at Austin, USA, Kaiyuan Wang Google, Inc., Haris Vikalo University of Texas at Austin, USA, Sarfraz Khurshid University of Texas at Austin | ||
15:55 15mTalk | Data-Driven Inference of Representation Invariants SIGPLAN Papers Anders Miltner The University of Texas at Austin, Texas, USA, Saswat Padhi Amazon Web Services, USA, Todd Millstein University of California, Los Angeles, David Walker Princeton University, USA | ||
16:10 15mTalk | Reconciling Enumerative and Deductive Program Synthesis SIGPLAN Papers Kangjing Huang Purdue University, USA, Xiaokang Qiu Purdue University, USA, Peiyuan Shen Purdue University, USA, Yanjun Wang Purdue University, USA | ||
16:25 15mTalk | Synthesizing Structured CAD Models with Equality Saturation and Inverse Transformations SIGPLAN Papers Chandrakana Nandi Certora, inc., Max Willsey University of Washington, Adam Anderson University of Washington, James R. Wilcox University of Washington, Eva Darulova Uppsala University, Dan Grossman University of Washington, Zachary Tatlock University of Washington | ||
16:40 20mLive Q&A | Discussion, Questions and Answers SIGPLAN Papers |