419 research outputs found
End Users Creating More Effective Software
Slides for my talk on various ways to create end user software
Automatic, look-and-feel independent dialog creation for graphical user interfaces
Jade is a new interactive tool that automatically creates graphical input dialogs such as dialog boxes and menus. Application programmers write a textual specification of a dialog’s contents. This specification contains absolutely no graphical information and thus is look-and-feel inde-pendent. The graphic artist uses a direct manipulation graphical editor to define the rules, graphical objects, in-teraction techniques, and decorations that will govern the dialog’s look-and-feel, and stores the results in a look and feel database. Jade combines the application programmer’s specification with the look-and-feel database to automatically generate a graphical dialog. If necessary, the graphic artist can then edit the resulting dialog using a graphical editor and these edits will be remembered by Jade, even if the original textual specification is modified. By eliminating all graphical references from the dialog’s content specification, Jade requires only the absolutely minimum specification from the application programmer. This also allows a dialog box or menu’s look and feel to be rapidly and effortlessly changed by simply switching look and feel databases. Finally, Jade permits complex inter-field relationships to be specified in a simple manner
A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges
The software engineering community recently has witnessed widespread
deployment of AI programming assistants, such as GitHub Copilot. However, in
practice, developers do not accept AI programming assistants' initial
suggestions at a high frequency. This leaves a number of open questions related
to the usability of these tools. To understand developers' practices while
using these tools and the important usability challenges they face, we
administered a survey to a large population of developers and received
responses from a diverse set of 410 developers. Through a mix of qualitative
and quantitative analyses, we found that developers are most motivated to use
AI programming assistants because they help developers reduce key-strokes,
finish programming tasks quickly, and recall syntax, but resonate less with
using them to help brainstorm potential solutions. We also found the most
important reasons why developers do not use these tools are because these tools
do not output code that addresses certain functional or non-functional
requirements and because developers have trouble controlling the tool to
generate the desired output. Our findings have implications for both creators
and users of AI programming assistants, such as designing minimal cognitive
effort interactions with these tools to reduce distractions for users while
they are programming.Comment: Accepted to ICSE'2
Ontogenetic trait variation influences tree community assembly across environmental gradients
Intraspecific trait variation is hypothesized to influence the relative importance of community assembly mechanisms. However, few studies have explicitly considered how intraspecific trait variation among ontogenetic stages influences community assembly across environmental gradients. Because the relative importance of abiotic and biotic assembly mechanisms can differ among ontogenetic stages within and across environments, ontogenetic trait variation may have an important influence on patterns of functional diversity and inferred assembly mechanisms. We tested the hypothesis that variation in functional diversity across a topo-edaphic gradient differs among ontogenetic stages and that these patterns reflect a shift in the relative importance of different assembly mechanisms. In a temperate forest in the Missouri Ozarks, USA, we compared functional diversity of leaf size and specific leaf area (SLA) of 34 woody plant species at two ontogenetic stages (adults and saplings) to test predictions about how the relative importance of abiotic and biotic filtering changes among adult and sapling communities. Local communities of adults had lower mean SLA and lower functional dispersion of SLA than expected by chance, particularly at the resource-limited end of the topo-edaphic gradient, suggesting an important role for abiotic filtering among co-occurring adults. In contrast, local communities of saplings often had higher functional dispersion of leaf size and SLA than expected by chance regardless of their location along the topo-edaphic gradient, suggesting an important role for biotic filtering among co-occurring saplings. Moreover, the overall strength of trait-environment relationships varied between saplings and adults for both leaf traits, generally resulting in stronger environmental shifts in mean trait values and trait dispersion for adults relative to saplings. Our results illustrate how community assembly mechanisms may shift in their relative importance during ontogeny, leading to variable patterns of functional diversity across environmental gradients. Moreover, our results highlight the importance of integrating ontogeny, an important axis of intraspecific trait variability, into approaches that use plant functional traits to understand community assembly and species coexistence
07081 Abstracts Collection --- End-User Software Engineering
From 18.01.07 to 23.02.07, the Dagstuhl Seminar 07081 ``End-User Software Engineering\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models
Sensemaking in unfamiliar domains can be challenging, demanding considerable
user effort to compare different options with respect to various criteria.
Prior research and our formative study found that people would benefit from
reading an overview of an information space upfront, including the criteria
others previously found useful. However, existing sensemaking tools struggle
with the "cold-start" problem -- it not only requires significant input from
previous users to generate and share these overviews, but such overviews may
also turn out to be biased and incomplete. In this work, we introduce a novel
system, Selenite, which leverages Large Language Models (LLMs) as reasoning
machines and knowledge retrievers to automatically produce a comprehensive
overview of options and criteria to jumpstart users' sensemaking processes.
Subsequently, Selenite also adapts as people use it, helping users find, read,
and navigate unfamiliar information in a systematic yet personalized manner.
Through three studies, we found that Selenite produced accurate and
high-quality overviews reliably, significantly accelerated users' information
processing, and effectively improved their overall comprehension and
sensemaking experience.Comment: Accepted to CHI 202
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