This paper presents natural synthesis, which generalizes the proof-theoretic synthesis technique to support very expressive logic theories. This approach leverages the natural proof methodology and reduces an intractable, unbounded-size synthesis problem to a tractable, bounded-size synthesis problem, which is amenable to be handled by modern inductive synthesis engines. The synthesized program admits a natural proof and is a provably-correct solution to the original synthesis problem. We explore the natural synthesis approach in the domain of imperative data-structure manipulations and present a novel syntax-guided synthesizer based on natural synthesis. The input to our system is a program template together with a rich functional specification that the synthesized program must meet. Our system automatically produces a program implementation along with necessary proof artifacts, namely loop invariants and ranking functions, and guarantees the total correctness with a natural proof.
Experiments show that our natural synthesizer can efficiently produce provably-correct implementations for sorted lists and binary search trees. To our knowledge, this is the first system that can automatically synthesize these programs, their functional correctness and their termination in tandem from bare-bones control flow skeletons.
Wed 25 OctDisplayed time zone: Tijuana, Baja California change
15:30 - 17:22
|Model-Assisted Machine-Code Synthesis
Venkatesh Srinivasan University of Wisconsin - Madison, Ara Vartanian University of Wisconsin-Madison, USA, Thomas Reps University of Wisconsin - Madison and GrammaTech, Inc.DOI
|Synthesis of Data Completion Scripts using Finite Tree Automata
|SQLizer: Query Synthesis from Natural Language
Navid Yaghmazadeh University of Texas, Austin, Yuepeng Wang University of Texas at Austin, Işıl Dillig UT Austin, Thomas DilligDOI
|Synthesizing Configuration File Specifications with Association Rule Learning
Mark Santolucito Yale University, Ennan Zhai Yale University, USA, Rahul Dhodapkar MongoDB, USA, Aaron Shim Microsoft, USA, Ruzica Piskac Yale UniversityDOI
|Natural Synthesis of Provably-Correct Data-Structure Manipulations