An experimental study into the default reading of constraint diagrams

FISH, ANDREW and Masthoff, Judith (2005) An experimental study into the default reading of constraint diagrams In: Proceedings of Visual Languages and Human Centric Computing (VLHCC05), Dallas, USA, 20-24 September, 2009.

[img]
Preview
Text
FishMasthoffVLHCC05.pdf - Accepted Version

Download (132kB) | Preview

Abstract

Constraint diagrams are a complex diagrammatic notation designed to express logical statements especially for use in software specification and reasoning. Not surprisingly, since this is an expressive language, there are some difficulties in reading the semantics of a diagram unambiguously. Some extra annotations (in the form of a reading tree) disambiguate the diagrams. However, this extra requirement (of drawing a reading tree) places a burden on the user. An attempt to remove the need for such a reading tree (or perhaps to automatically generate a reading tree, which could be altered by a user if they wished to) has been given via an algorithm to generate a default reading from the diagram. This algorithm is based on a number of principles – most of which are properties of the diagram. We wish to know whether these principles are intuitive and whether the default reading reflects a good proportion of users’ intuitions, and we have performed a user-based study to test this. This report summarizes this study.

Item Type: Contribution to conference proceedings in the public domain ( Full Paper)
Additional Information: © 2009 IEEE.
Uncontrolled Keywords: Constaint diagrams; Diagrammatic notation
Subjects: G000 Computing and Mathematical Sciences > G400 Computing
DOI (a stable link to the resource): 10.1109/VLHCC.2005.17
Faculties: Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Visual Modelling
Depositing User: Converis
Date Deposited: 24 Nov 2007
Last Modified: 21 May 2014 11:01
URI: http://eprints.brighton.ac.uk/id/eprint/3272

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year