Unification-based grammars and tabular parsing for graphical languages

Wittenburg, Kent; Weitzman, Louis; Talley, Jim

doi:10.1016/s1045-926x(05)80004-7

Cited by 40 publications

(22 citation statements)

References 6 publications

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“…Wittenburg, et al [150] give a unification-based, bottom-up chart parser which is similar to Lutz's and our chart parser. Grammar rules place a strict (total) ordering on the nodes in their right-hand sides.…”

Section: Graph Parsersmentioning

confidence: 76%

Automated Program Recognition by Graph Parsing

Wills¹

1992

107

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JAN 22 1993_ 93-01046 93 J21 034- REPORT DOCUMENTATION PAGEForM N=o07-1 01MB NO 0704-0188 Pub•ic reporting burden for this collection of information is estimated to averaqe I hour oer response. including the time for revewing Instructions. searchrg e.-st'ng sata sour'., gathering and maintaining the data needed, and completing and reviewing the collection of information Send comments retarding this burden estimate or any other asoect Of this ol lection of information, including suggestions for reducing this burden, We demonstrate that this graph parsing approach is a feasible and useful way to automate program recognition. In studying this approach, we have experimented with two medium-sized, real-world simulator programs. There are three aspects of our study. First, we evaluate our representation's ability to suppress many common forms of program variation which hinder recognition. Second, we investigate the expressiveness of our graph grammar formalism for capturing programming cliches. Third, we empirically and analytically study the computational cost of our recognition approach with respect to the real-world simulator programs. The recognition of standard computational structures (cliches) in a program can help an experienced programmer understand the program. Based on the known relationships between the cliches, a hierarchical description of the program's design can be recovered. We develop and study a graph parsing approach to automating program recognition in which programs are represented as attributed dataflow graphs and a library of cliches is encoded as an attributed graph grammar. Graph parsing is used to recognize clich6s in the code. We demonstrate that this graph parsing approach is a feasible and useful way to automate program recognition. In studying this approach, we have experimented with two medium-sized, real-world simulator programs. There are three aspects of our study. First, we evaluate our representation's ability to suppress many common forms of program variation which hinder recognition. Second, we investigate the expressiveness of our graph grammar formalism for capturing programming clich6s. Third, we empirically and analytically study the computational cost of our recognition approach with respect to the real-world simulator programs.

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Section: Graph Parsersmentioning

confidence: 76%

Automated Program Recognition by Graph Parsing

Wills¹

1992

107

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“…Other approaches closely related to this one use (constraint) logic or relational formalisms (e.g. [25,26,27,28,29,30]) to represent spatial relationships. Experience has shown (reported by Wittenburg in [29]) that some grammar approaches have limitations (e.g.…”

Section: Related Workmentioning

confidence: 99%

A Fully Formalized Theory for Describing Visual Notations

Haarslev

1998

Visual Language Theory

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This paper addresses issues in visual language theory with the help of logic formalisms that were developed for reasoning tasks by the artificial intelligence and spatial databases community, especially for spatial and diagrammatical reasoning. We describe an approach based on three formal components. Topology is used to define basic geometric objects. Theory about spatial relations from the domain of spatial databases is employed to define possible relationships between visual language elements. Description logic theory from the AI community is used to combine topology and spatial relations. The resulting theory has been successfully applied to formally specifying semantics of visual languages. The theory's application is illustrated with a specification of entity-relationship diagrams.

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“…The bottom-up, incremental parsing algorithm of [23,25] is used here with only one change. Derivations are considered successful even if they do not cover all the input elements.…”

Section: : the Top-level Loopmentioning

confidence: 99%

“…Nonmonotonic changes to the input should also be handled with minimal disruption so users can move, resize, and delete elements of their input. Our approach to these problems is to use Relational Grammars [23][24][25] for our rule language and the incremental, bottom-up parsing algorithm of [23,25] in our top-level interaction loop.…”

Section: : Introductionmentioning

confidence: 99%