Figure 1. Aquamarine's History Panel showing operation #10 (brush stroke for the nose) selectively undone. Selective Undo Support for Painting Applications

ABSTRACT Today's widely deployed painting applications use a linear undo model that allows users to backtrack previous operations in reverse chronological order. This undo model is not useful if the user has performed desired operations after undesired ones. Selective undo, in contrast, allows users to select specific operations in the past and only undo those, while keeping the remaining operations intact. Although selective undo has been widely explored in the context of text editing and object-oriented drawing, we explore selective undo for painting (bitmap) editing, which has received less attention and introduces many interesting user interface design challenges. Our system, called Aquamarine, explores the script model for selective undo, where selectively undone operations are skipped in the history, rather than the more explored inverse model, which puts an inverse of the selected operations at the end of the history. We discuss the design implications and show through two informal user studies that selective undo is usable and desirable

Automating iterative tasks with programming by demonstration

Programming by demonstration is an end-user programming technique that allows people to create programs by showing the computer examples of what they want to do. Users do not need specialised programming skills. Instead, they instruct the computer by demonstrating examples, much as they might show another person how to do the task. Programming by demonstration empowers users to create programs that perform tedious and time-consuming computer chores. However, it is not in widespread use, and is instead confined to research applications that end users never see. This makes it difficult to evaluate programming by demonstration tools and techniques. This thesis claims that domain-independent programming by demonstration can be made available in existing applications and used to automate iterative tasks by end users. It is supported by Familiar, a domain-independent, AppleScript-based programming-by-demonstration tool embodying standard machine learning algorithms. Familiar is designed for end users, so works in the existing applications that they regularly use. The assertion that programming by demonstration can be made available in existing applications is validated by identifying the relevant platform requirements and a range of platforms that meet them. A detailed scrutiny of AppleScript highlights problems with the architecture and with many implementations, and yields a set of guidelines for designing applications that support programming-by-demonstration. An evaluation shows that end users are capable of using programming by demonstration to automate iterative tasks. However, the subjects tended to prefer other tools, choosing Familiar only when the alternatives were unsuitable or unavailable. Familiar's inferencing is evaluated on an extensive set of examples, highlighting the tasks it can perform and the functionality it requires

Autoría de documentos web dinámicos mediante ontologías y técnicas de programación por demostración

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: 16-09-200

Scripting Graphical Applications by Demonstration

Writing scripts (often called "macros") can be helpful for automating repetitive tasks. Scripting facilities for text editors like Emacs and Microsoft Word have been widely used and available. However, for graphical applications, scripting has been tried many times but has never been successful. This is mainly due to the data description problem of determining how to generalize the particular objects selected at demonstration time. Previous systems have mostly tried to solve this using inferencing, but this has a number of problems, including guessing wrong and providing appropriate feedback and control to users. Therefore, the Topaz framework does not use inferencing and instead allows the user to specify how the appropriate objects should be found. This is achieved by recording changes to which objects are selected and searches for objects, so that scripts can be written with respect to the selected object, in the same way as Emacs keyboard macros. Furthermore, all values can be expl..