Fast

Author:

D'Antoni Loris1,Veanes Margus2,Livshits Benjamin2,Molnar David2

Affiliation:

1. University of Pennsylvania

2. Microsoft Research

Abstract

Tree automata and tree transducers are used in a wide range of applications in software engineering, from XML processing to language type-checking. While these formalisms are of immense practical use, they can only model finite alphabets, and since many real-world applications operate over infinite domains such as integers, this is often a limitation. To overcome this problem we augment tree automata and transducers with symbolic alphabets represented as parametric theories. Admitting infinite alphabets makes these models more general and succinct than their classical counterparts. Despite this, we show how the main operations, such as composition and language equivalence, remain computable given a decision procedure for the alphabet theory. We introduce a high-level language called Fast that acts as a front-end for the above formalisms. Fast supports symbolic alphabets through tight integration with state-of-the-art satisfiability modulo theory (SMT) solvers. We demonstrate our techniques on practical case studies, covering a wide range of applications.

Funder

Division of Computing and Communication Foundations

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference42 articles.

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1. Incremental Minimization of Symbolic Automata;Lecture Notes in Computer Science;2020

2. Equivalent State of Finite Automata and Its Judgment Theorem;Proceedings of the 2018 2nd International Conference on Algorithms, Computing and Systems - ICACS '18;2018

3. Automatic program inversion using symbolic transducers;ACM SIGPLAN Notices;2017-09-14

4. Automatic program inversion using symbolic transducers;Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation;2017-06-14

5. Regular Programming for Quantitative Properties of Data Streams;Programming Languages and Systems;2016

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