Lrc — a generator for incremental language-oriented tools

Kuiper, M.F.; Saraiva, João

doi:10.1007/bfb0026440

Cited by 37 publications

(33 citation statements)

References 5 publications

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“…In practise, meta-grammarware is often packaged in frameworks for software transformation, program analysis, language processing, and program generation. Examples of such frameworks include the following: ASF+SDF Meta-Environment [Klint 1993;van den Brand et al 2001], Cocktail [Grosch and Emmelmann 1991], Cornell Synthesizer Generator [Reps and Teitelbaum 1984], DMS [Baxter 1992], Eli [Gray et al 1992], FermaT [Ward 1999], GENTLE [Schröer 1997], Lrc [Kuiper and Saraiva 1998], Progres [Progres group 2004], Refine [Smith et al 1985;Abraido-Fandino 1987], RIGAL [Auguston 1990], S/SL , Stratego [Visser 2001a], Strafunski [Lämmel and Visser 2003], TXL .…”

Section: Meta-grammarwarementioning

confidence: 99%

Toward an engineering discipline for grammarware

Klint

Lämmel

Verhoef

2005

ACM Trans. Softw. Eng. Methodol.

196

135

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and Chris Verhoef Vrije Universiteit AmsterdamGrammarware comprises grammars and all grammar-dependent software. The term grammar is meant here in the sense of all established grammar formalisms and grammar notations including context-free grammars, class dictionaries, XML schemas as well as some forms of tree and graph grammars. The term grammar-dependent software refers to all software that involves grammar knowledge in an essential manner. Archetypal examples of grammar-dependent software are parsers, program converters, and XML document processors. Despite the pervasive role of grammars in software systems, the engineering aspects of grammarware are insufficiently understood. We lay out an agenda that is meant to promote research on increasing the productivity of grammarware development and on improving the quality of grammarware. To this end, we identify the problems with the current grammarware practises, the barriers that currently hamper research, the promises of an engineering discipline for grammarware, its principles, and the research challenges that have to be addressed.

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Section: Meta-grammarwarementioning

confidence: 99%

Toward an engineering discipline for grammarware

Klint

Lämmel

Verhoef

2005

ACM Trans. Softw. Eng. Methodol.

196

135

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“…A language-parameteric solution to address this is described in [70], which can be used to fix name captures after the transformation. Uses, Examples A demo project illustrating DSL development in Rascal using a simple state machine language includes a slight variation of the rename refactoring 13 .…”

Section: An Example Solution To Persistent User-defined Formattingmentioning

confidence: 99%

“…Perhaps the earliest tool which we would now think of as a language workbench was SEM [4], which was later followed by tools such as MetaPlex [5], Metaview [6], QuickSpec [7], and MetaEdit [8], Centaur [9], the Synthesizer generator [10], the ASF+SDF Meta-Environment [11], Gem-Mex/Montages [12], LRC [13], and Lisa [14]. Most of these systems operated on textual languages and were intended to work with formal specifications of General Purpose Languages (GPLs) [15].…”

mentioning

confidence: 99%

Evaluating and comparing language workbenches

Erdweg

Storm

Völter³

et al. 2015

Computer Languages, Systems & Structures

113

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Language workbenches are environments for simplifying the creation and use of computer languages. The annual Language Workbench Challenge (LWC) was launched in 2011 to allow the many academic and industrial researchers in this area an opportunity to quantitatively and qualitatively compare their approaches. We first describe all four LWCs to date, before focussing on the approaches used, and results generated, during the third LWC. We give various empirical data for ten approaches from the third LWC. We present a generic feature model within which the approaches can be understood and contrasted. Finally, based on our experiences of the existing LWCs, we propose a number of benchmark problems for future LWCs.

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“…Recent advances in programing languages extend naive editors, to powerful language-based environments [12,13,15,17]. Language-based environments use knowledge of the programming language to provide the users with more powerful mechanisms to develop their programs.…”

Section: Introductionmentioning

confidence: 99%

Discovery-based edit assistance for spreadsheets

Cunha

Saraiva

Visser³

2009

2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Spreadsheets can be viewed as a highly flexible endusers programming environment which enjoys wide-spread adoption. But spreadsheets lack many of the structured programming concepts of regular programming paradigms. In particular, the lack of data structures in spreadsheets may lead spreadsheet users to cause redundancy, loss, or corruption of data during edit actions.In this paper, we demonstrate how implicit structural properties of spreadsheet data can be exploited to offer edit assistance to spreadsheet users. Our approach is based on the discovery of functional dependencies among data items which allow automatic reconstruction of a relational database schema. From this schema, new formulas and visual objects are embedded into the spreadsheet to offer features for auto-completion, guarded deletion, and controlled insertion. Schema discovery and spreadsheet enhancement are carried out automatically in the background and do not disturb normal user experience.

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Lrc — a generator for incremental language-oriented tools

Cited by 37 publications

References 5 publications

Toward an engineering discipline for grammarware

Toward an engineering discipline for grammarware

Evaluating and comparing language workbenches

Discovery-based edit assistance for spreadsheets

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