453 research outputs found

    Canonical Abstract Syntax Trees

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    This paper presents Gom, a language for describing abstract syntax trees and generating a Java implementation for those trees. Gom includes features allowing the user to specify and modify the interface of the data structure. These features provide in particular the capability to maintain the internal representation of data in canonical form with respect to a rewrite system. This explicitly guarantees that the client program only manipulates normal forms for this rewrite system, a feature which is only implicitly used in many implementations

    Efficient Pattern Matching in Python

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    Pattern matching is a powerful tool for symbolic computations. Applications include term rewriting systems, as well as the manipulation of symbolic expressions, abstract syntax trees, and XML and JSON data. It also allows for an intuitive description of algorithms in the form of rewrite rules. We present the open source Python module MatchPy, which offers functionality and expressiveness similar to the pattern matching in Mathematica. In particular, it includes syntactic pattern matching, as well as matching for commutative and/or associative functions, sequence variables, and matching with constraints. MatchPy uses new and improved algorithms to efficiently find matches for large pattern sets by exploiting similarities between patterns. The performance of MatchPy is investigated on several real-world problems

    Verification of Timed Automata Using Rewrite Rules and Strategies

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    ELAN is a powerful language and environment for specifying and prototyping deduction systems in a language based on rewrite rules controlled by strategies. Timed automata is a class of continuous real-time models of reactive systems for which efficient model-checking algorithms have been devised. In this paper, we show that these algorithms can very easily be prototyped in the ELAN system. This paper argues through this example that rewriting based systems relying on rules and strategies are a good framework to prototype, study and test rather efficiently symbolic model-checking algorithms, i.e. algorithms which involve combination of graph exploration rules, deduction rules, constraint solving techniques and decision procedures

    MatchPy: A Pattern Matching Library

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    Pattern matching is a powerful tool for symbolic computations, based on the well-defined theory of term rewriting systems. Application domains include algebraic expressions, abstract syntax trees, and XML and JSON data. Unfortunately, no lightweight implementation of pattern matching as general and flexible as Mathematica exists for Python Mathics,MacroPy,patterns,PyPatt. Therefore, we created the open source module MatchPy which offers similar pattern matching functionality in Python using a novel algorithm which finds matches for large pattern sets more efficiently by exploiting similarities between patterns.Comment: arXiv admin note: substantial text overlap with arXiv:1710.0007

    Formal Validation of Pattern Matching code

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    When addressing the formal validation of generated software, two main alternatives consist either to prove the correctness of compilers or to directly validate the generated code. Here, we focus on directly proving the correctness of compiled code issued from powerful pattern matching constructions typical of ML like languages or rewrite based languages such as ELAN, MAUDE or Tom. In this context, our first contribution is to define a general framework for anchoring algebraic pattern-matching capabilities in existing languages like C, Java or ML. Then, using a just enough powerful intermediate language, we formalize the behavior of compiled code and define the correctness of compiled code with respect to pattern-matching behavior. This allows us to prove the equivalence of compiled code correctness with a generic first-order proposition whose proof could be achieved via a proof assistant or an automated theorem prover. We then extend these results to the multi-match situation characteristic of the ML like languages. The whole approach has been implemented on top of the Tom compiler and used to validate the syntactic matching code of the Tom compiler itself
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