Claims for the virtues of visual programming languages have generally been strong, simple-minded statements that visual programs are inherently better than textual ones. They have paid scant attention to previous empirical literature showing difficulties in comprehending visual programs. This paper reports comparisons between the comprehensibility of textual and visual programs, drawing on the methods developed by Green (1977) for comparing detailed comprehensibility of conditional structures. The visual language studied was LabView, a circuit-diagram-like language which can express conditionals either as `forwards' structures (condition implies action, with nesting) or as `backwards' structures (action is governed by conditions, with boolean operators in place of nesting). Green (1977) found that forwards structures gave relatively better access to `sequential' information, and Gilmore and Green (1984) showed `backwards' structures gave relatively better access to `circumstantial' inf...

attribute grammar to specify attribute dependency and data transformation. Based on the fine-grain algorithms and the SDTM model, we have built a fine-grain visualization system that exhibits faster speed, less memory usage, and higher CPU utilization than a typical coarse-grain system. iii To my wife, Lori Lai, and my parents iv ACKNOWLEDGEMENTS First I'd like to thank my advisor, Professor Eric Golin, for his interest in me and his guidance throughout this project, and my co-advisor, Professor Michael Norman, for his continuous support for me to work in his group at the National Center for Supercomputing Applications (NCSA). Other members of the committee, Professor Daniel Reed, Professor William Kubitz, Professor Ralph Johnson, Professor Simon Kaplan, Dr. Polly Baker, have given me valuable comments. Professor Robert Haber and Professor Donald Hearn have exposed me to the field of scientific visualization in the early days of my graduate study here at the UIUC c