Programming by example: intelligence in demonstrational interfaces

Myers, Brad A.; McDaniel, Richard G.; Wolber, David

doi:10.1145/330534.330545

Cited by 65 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, this system relies on the availability of crowdsourcing workers with the right expertise to fulfill the task at hand, which could be a problem when designing custom interactions and more complex prototypes. Other approaches, such as programming by demonstration [45] or the use of state machines and inference engines, have not been studied in a collaborative context.…”

Section: Prototyping Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Enact

Leiva

Maudet

Mackay

et al. 2019

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

Professional designers and developers often struggle when transitioning between the design and implementation of an interactive system. We conducted three studies that focused on the design of custom interactions to understand the mismatches between their processes, tools and representations. We found that current practices induce unnecessary rework and cause discrepancies between design and implementation. We identified three recurring types of breakdowns: omitting critical details, ignoring edge cases, and disregarding technical limitations. We propose four design principles to create tools that mitigate these problems: Provide multiple viewpoints, maintain a single source of truth, reveal the invisible and support design by enaction. We applied these principles to create Enact, a live environment for prototyping touch-based interactions. We conducted two studies to assess Enact and to compare designer-developer collaboration with Enact versus current tools. Results suggest that Enact helps participants detect more edge cases, increases designers' participation and provides new opportunities for co-creation.

show abstract

Section: Prototyping Toolsmentioning

confidence: 99%

“…Enact uses a form of programming-by-demonstration but without sophisticated inferencing so as to provide a simpler user interface for professional designers and developers [45]. However, inferencing could open new possibilities to maintain a single source of truth.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Enact

Leiva

Maudet

Mackay

et al. 2019

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

show abstract

“…And also related is the work by Brad Myers et.al. where they built systems in which the user defines behaviors by demonstration [4]; in some of them, the system uses AI algorithms, such as inferring complex behavior from a few examples; in others, the user has to provide the full specification, and the examples are used primarily to help the user understand the programming situation.…”

Section: Related Workmentioning

confidence: 99%

On extracting unit tests from interactive live programming sessions

Kuhn

2013

2013 35th International Conference on Software Engineering (ICSE)

View full text Add to dashboard Cite

Software engineering methodologies propose that developers should capture their efforts in ensuring that programs run correctly in repeatable and automated artifacts, such as unit tests. However, when looking at developer activities on a spectrum from exploratory testing to scripted testing we find that many engineering activities include bursts of exploratory testing. In this paper we propose to leverage these exploratory testing bursts by automatically extracting scripted tests from a recording of these sessions. In order to do so, we wiretap the development environment so we can record all program input, all user-issued functions calls, and all program output of an exploratory testing session. We propose to then use machine learning (i.e. clustering) to extract scripted test cases from these recordings in real-time. We outline two early-stage prototypes, one for a static and one for a dynamic language. And we outline how this idea fits into the bigger research direction of programming by example.

show abstract

“…As noted earlier, the user does not need to understand the underlying models to manage the system (though such an understanding of course may help); instead, all modeling is done through straightforward task demonstration, an approach similar to that used in recent tools [Hudson et al 1999;John et al 2004a] (see Myers et al [2000], Paganelli and Paternò [2003], and St. Amant et al [2000] for related work).…”

Section: Tasks: Modeling By Demonstrationmentioning

confidence: 99%

Rapid prototyping and evaluation of in-vehicle interfaces

Salvucci

2009

ACM Trans. Comput.-Hum. Interact.

View full text Add to dashboard Cite

As driver distraction from in-vehicle devices becomes an increasingly critical issue, researchers have aimed to establish better scientific understanding of distraction along with better engineering tools to build less distracting devices. This article presents a new system, Distract-R, that allows designers to rapidly prototype and evaluate new in-vehicle interfaces. The core engine of the system relies on a rigorous cognitive model of driver behavior which, when integrated with models of task behavior on the prototyped interfaces, generate predictions of driver performance and distraction. Distract-R allows a designer to prototype basic interfaces, demonstrate possible tasks on these interfaces, specify relevant driver characteristics and driving scenarios, and finally simulate, visualize, and analyze the resulting behavior as generated by the cognitive model. The article includes three modeling studies that demonstrate the system's ability to account for various aspects of driver performance for several types of in-vehicle interfaces. More generally, Distract-R illustrates how cognitive models can be used as internal simulation engines for design tools intended for nonmodelers, with the ultimate goal of helping to understand and predict user behavior in multitasking environments.

show abstract

Programming by example: intelligence in demonstrational interfaces

Cited by 65 publications

References 10 publications

Enact

Enact

On extracting unit tests from interactive live programming sessions

Rapid prototyping and evaluation of in-vehicle interfaces

Contact Info

Product

Resources

About