Memoized zipper-based attribute grammars and their higher order extension

Attribute grammars are a powerfull, well-known formalism to implement and reason about programs which, by design, are conveniently modular. In this work we focus on a state of the art zipper-based embedding of classic attribute grammars and higher-order attribute grammars. We improve their execution performance through controlling attribute (re)evaluation by means of memoization techniques. We present the results of our optimizations by comparing their impact in various implementations of different, well-studied, attribute grammars and their Higher-Order extensions. (C) 2018 Elsevier B.V. All rights reserved.- (undefined

Component-based programming for higher-order attribute grammars

This paper presents techniques for a component-based style of programming in the context of higher-oder attribute grammars (HAG). Attribute grammar components are "plugged in" into larger attribute grammar systems through higher-order attribute grammars. Higher-order attributes are used as (intermediate) "gluing" data structures.This paper also presents two attribute grammar components that can be re-used across different language-based tool specifications: a visualizer and animator of programs and a graphical user interface AG component. Both components are reused in the definition of a simple language processor. The techniques presented in this paper are implemented in LRC: a purely functional, higher-order attribute grammar-based system that generates language-based tools.(undefined

Embedding attribute grammars and their extensions using functional zippers

Attribute grammars are a suitable formalism to express complex software language analysis and manipulation algorithms, which rely on multiple traversals of the underlying syntax tree. Attribute grammars have been extended with mechanisms such as reference, higher order and circular attributes. Such extensions provide a powerful modular mechanism and allow the specification of complex computations. This paper studies an elegant and simple, zipper-based embedding of attribute grammars and their extensions as first class citizens. In this setting, language specifications are defined as a set of independent, off-the-shelf components that can easily be composed into a powerful, executable language processor. Techniques to describe automatic bidirectional transformations between grammars in this setting are also described. Several real examples of language specification and processing programs have been implemented. (C) 2016 Elsevier B.V. All rights reserved.This author is supported by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project ON.2 IC&DT Programa Integrado "BEST CASE - Better Science Through Cooperative Advanced Synergetic Efforts (Ref. BIM-2013_BestCase_RL3.2_UMINHO) and project FATBIT - Foundations, Applications and Tools for Bidirectional Transformation (Ref. FCOMP-01-0124-FEDER-020532).This author is partially supported by NSF Award #1047961

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page