The Amulet user interface development environment uses hierarchical command objects to support the creation of highly-interactive graphical user interfaces. When input arrives or a widget is operated by the user, instead of invoking a call-back procedure as in most other toolkits, Amulet allocates a command object and calls its DO method. Unlike previous uses of command objects, Amulet organizes the commands into a hierarchy, so that low-level operations like dragging or selection invoke low-level commands, which in turn might invoke widget-level commands, which invoke high-level, application-specific commands, and so on. The top-level commands correspond to semantic actions of the program. The result is better modularization because different levels of the user interface are independent, and better code reuse because the lower-level commands, and even many high-level commands such as cut, copy, paste, text edit, and change-color, can be reused from the library. Furthermore, the commands in Amulet support a new form of Undo, where the user can select any previous operation and selectively undo it, repeat it on the same objects, or repeat it on new objects. In addition, operations like scrolling and selections can be undone or repeated, which can be very useful. Thus, the command objects in Amulet make it easier for developers by providing more reusable components, while at the same time providing new capabilities for users.
One-way, dataflow constraints are commonly used in graphical interface toolkits, programming environments, and circuit applications. Previous papers on dataflow constraints have focused on the design and implementation of individual algorithms. In contrast, this article focuses on the lessons we have learned from a decade of implementing competing algorithms in the Garnet and Amulet graphical interface toolkits. These lessons reveal the design and implementation tradeoffs for different one-way, constraint satisfaction algorithms. The most important lessons we have learned are that (1) mark-sweep algorithms are more efficient than topological ordering algorithms; (2) lazy and eager evaluators deliver roughly comparable performance for most applications; and (3) constraint satisfaction algorithms have more than adequate speed, except that the storage required by these algorithms can be problematic.
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