Large Language Models (LLMs) are increasingly utilized for domain-specific tasks, yet evaluating their outputs remains challenging. A common strategy is to apply evaluation criteria to assess alignment with domain-specific standards, yet little is understood about how criteria differ across sources or where each type is most useful in the evaluation process. This study investigates criteria developed by domain experts, lay users, and LLMs to identify their complementary roles within an evaluation workflow. Results show that experts produce fact-based criteria with long-term value, lay users emphasize usability with a shorter-term focus, and LLMs target procedural checks for immediate task requirements. We also examine how criteria evolve between a priori and a posteriori phases, noting drift across stages as well as convergence in the a posteriori phase. Based on our observations, we propose design guidelines for a staged evaluation workflow combining the complementary strengths of these sources to balance quality, cost, and scalability.

Technology offers new opportunities to support healthier food choices, particularly for individuals in low-income communities who face systemic barriers to obtaining nutritious, affordable groceries. We introduce a novel conceptual model of grocery planning that frames food purchasing as a multi-objective optimization problem that considers cost, nutrition components, and a consumer’s personal dietary goals. Guided by Zimmerman’s model of Self-Regulated Learning and prior research on food agency, we designed the Food Information System, a planning tool that provides optimized product recommendations aligned with users’ goals by integrating store inventory, prices, and nutritional data. We evaluated our system in an eight-week within-subjects intervention with 55 participants from a food-insecure community, followed by focus group sessions. While overall Healthy Eating Index scores remained largely stable, participants reported improved nutritional awareness and greater perceived agency in planning and purchasing groceries. We discuss design implications to support food agency by promoting long-term food literacy and by enhancing autonomy in making food choices.

Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats-tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

Generative AI is increasingly present in children’s learning environments, yet little is known about how families navigate this technology in middle childhood (ages 7–13), when parental guidance remains strong but children seek independence. Drawing on self-directed learning (SDL), we explore how parents in our exploratory sample perceived children’s emerging self-directness and agency. Through focus groups with 13 parent–child pairs, we examine parents’ views on children’s AI literacy development, readiness factors, and mediation strategies. Parents described emergent pathways shaped by screen time, self-directness, and knowledge growth. They often confined AI to learning-only contexts, positioning it as a tutor while overlooking non-learning uses and risks such as privacy and infrastructural embedding. Many acknowledged limited AI literacy and turned to joint engagement as opportunities for co-learning. Our findings surface possible parental pathways of children’s AI literacy, highlight gaps between pragmatic expectations and critical literacies, and offer situated design considerations for AI systems that scaffold SDL while balancing oversight with autonomy.

Large Language Models (LLMs) are increasingly developed for use in complex professional domains, yet little is known about how teams design and evaluate these systems in practice. This paper examines the challenges and trade-offs in LLM development through a 12-week ethnographic study of a team building a pedagogical chatbot. The researcher observed design and evaluation activities and conducted interviews with both developers and domain experts. Analysis revealed four key practices: creating workarounds for data collection, turning to augmentation when expert input was limited, co-developing evaluation criteria with experts, and adopting hybrid expert–developer–LLM evaluation strategies. These practices show how teams made strategic decisions under constraints and demonstrate the central role of domain expertise in shaping the system. Challenges included expert motivation and trust, difficulties structuring participatory design, and questions around ownership and integration of expert knowledge. We propose design opportunities for future LLM development workflows that emphasize AI literacy, transparent consent, and frameworks recognizing evolving expert roles.

Navigating the complexities of knowledge management (KM) and Knowledge Transfer (KT) in community social services is a challenging task, as workers must handle a fast-paced, resource-constrained environment while supporting the urgent and multifaceted needs of vulnerable populations. Despite research into the use of technology in non-profit and community settings, little attention has been given to how community service workers (CSWs) capture, share, and manage knowledge in practice. This study addresses this gap by conducting a three-month ethnographic study of two community organizations in South Bend, Indiana, complemented by semi-structured interviews, revealing the informal, ad-hoc KM methods CSWs use to manage information flows. Using a human-socio-technical KM framework, we compare these practices with the more structured KM systems found in large corporations, identifying key differences in formality and technological integration. Our findings highlight the need for accessible, sustainable KM solutions that fit the informal knowledge-sharing practices of CSWs while enhancing knowledge retention, knowledge transfer, and collaboration. This work contributes to HCI and CSCW by providing design considerations for developing technology that better supports KM in community social services.

Large Language Models (LLMs) have the potential to contribute to the fields of nutrition and dietetics in generating food product explanations that facilitate informed food selections. However, the extent to which these models offer effective and accurate information remains unverified. In collaboration with registered dietitians (RDs), we evaluate the strengths and weaknesses of LLMs in providing accurate and personalized nutrition information. Through a mixed-methods approach, RDs validated GPT-4 outputs at various levels of prompt specificity, which led to the development of design guidelines used to prompt LLMs for nutrition information. We tested these guidelines by creating a GPT prototype, The Food Product Nutrition Assistant, tailored for food product explanations. This prototype was refined and evaluated in focus groups with RDs. We find that the implementation of these dietitian-reviewed template instructions enhance the generation of detailed food product descriptions and tailored nutrition information.

Understanding nutrition labels remains challenging for consumers; however, digital shopping environments offer opportunities to explore how interactive nutrition labels may be used to enhance comprehension. We conducted an A/B study with 24 participants, comparing their ability to interpret and apply nutrition information using conventional, static labels versus interactive labels. We evaluated interactive nutrition labels’ impact through quantitative metrics and qualitative insights from interviews and think-aloud sessions. Our findings reveal a statistically significant improvement in assessing nutrient amounts and interpreting numerical information when users engage with interactive labels. These results underscore the potential interactivity has on promoting public understanding of nutritional content and highlight opportunities for refinement. Based on our findings, we propose new design directions and discuss technology’s role in making nutrition labels more effective for decision-making and nutrition education.

The increasing ubiquity of data in everyday life has elevated the importance of data literacy and accessible data representations, particularly for individuals with disabilities. While prior research predominantly focuses on the needs of the visually impaired, our survey aims to broaden this scope by investigating accessible data representations across a more inclusive spectrum of disabilities. After conducting a systematic review of 152 accessible data representation papers from ACM and IEEE databases, we found that roughly 78% of existing articles center on vision impairments. In this article, we conduct a comprehensive review of the remaining 22% of papers focused on underrepresented disability communities. We developed categorical dimensions based on accessibility, visualization, and human-computer interaction to classify the papers. These dimensions include the community of focus, issues addressed, contribution type, study methods, participants involved, data type, visualization type, and data domain. Our work redefines accessible data representations by illustrating their application for disabilities beyond those related to vision. Building on our literature review, we identify and discuss opportunities for future research in accessible data representations.

While various frameworks and heuristics exist within the HCI community to guide research and design for vulnerable populations, most are centered on the researcher’s involvement. In this work, we developed a conceptual framework for supporting the participation of vulnerable populations in the research and design of technologies. Building upon Maslow’s hierarchy of needs, we synthesized 84 research articles that focus on vulnerable populations and technology to develop our framework. This framework conceptualizes both the barriers, such as lack of technology access and digital literacy, and assets, like social relationships, that impact effective participation in research and design. Using our framework can guide researchers in identifying and fulfilling the technology-related needs of vulnerable populations, leading to more empowering research participation for these groups. The framework’s guiding questions offer researchers the opportunity to reflect on their approach prior to and during their collaboration with vulnerable populations in technology research and design.

Personal health tracking has long been a topic of investigation in the HCI community. There is an emerging class of apps that support gender transition, which we term transition-tracking apps. However, little work has been done examining the use and impact of such apps. We aimed to address this gap by conducting an interview study with sixteen participants who are currently undergoing different forms of gender transition. We provide an understanding of transition tracking habits, the usage and potential of transition-tracking apps in the context of transition support technologies, and provide design suggestions and open areas of research.

To identify technological opportunities to better support nutrition security and equality among those living in low-socioeconomic situations, we conducted 33 semi-structured interviews and seven in-home visits of lower- to middle-income households from a mid-sized city in northern Indiana. Inspired by assets-based approaches to public health, we investigated technology’s role in supporting how participants selected and purchased food, planned meals, and worked through logistical barriers to obtain food. Technology helped participants identify sales and coupons, search for recipes and health-related insights to address diet and health concerns, and share information. We contribute design implications (e.g., amplifying optimization behaviors and social engagement, leveraging substitutions) in support of food agency. We further contribute three emergent archetypes to convey central shopping tendencies (i.e., inventory shoppers, menu planners, and adaptive shoppers) and identify corresponding design implications. We situate our results into nutrition decision-making and education, social psychology, food consumer studies, and HCI literature.

Recent breakthroughs in deep-learning (DL) approaches have resulted in the dynamic generation of trace links that are far more accurate than was previously possible. However, DL-generated links lack clear explanations, and therefore non-experts in the domain can find it difficult to understand the underlying semantics of the link, making it hard for them to evaluate the link's correctness or suitability for a specific software engineering task. In this paper we present a novel NLP pipeline for generating and visualizing trace link explanations. Our approach identifies domain-specific concepts, retrieves a corpus of concept-related sentences, mines concept definitions and usage examples, and identifies relations between cross-artifact concepts in order to explain the links. It applies a post-processing step to prioritize the most likely acronyms and definitions and to eliminate non-relevant ones. We evaluate our approach using project artifacts from three different domains of interstellar telescopes, positive train control, and electronic healthcare systems, and then report coverage, correctness, and potential utility of the generated definitions. We design and utilize an explanation interface which leverages concept definitions and relations to visualize and explain trace link rationales, and we report results from a user study that was conducted to evaluate the effectiveness of the explanation interface. Results show that the explanations presented in the interface helped non-experts to understand the underlying semantics of a trace link and improved their ability to vet the correctness of the link.

Recipe recommendation systems play an essential role in helping people decide what to eat. Existing recipe recommendation systems typically focused on content-based or collaborative filtering approaches, ignoring the higher-order collaborative signal such as relational structure information among users, recipes and food items. In this paper, we formalize the problem of recipe recommendation with graphs to incorporate the collaborative signal into recipe recommendation through graph modeling. In particular, we first present URI-Graph, a new and large-scale user-recipe-ingredient graph. We then propose RecipeRec, a novel heterogeneous graph learning model for recipe recommendation. The proposed model can capture recipe content and collaborative signal through a heterogeneous graph neural network with hierarchical attention and an ingredient set transformer. We also introduce a graph contrastive augmentation strategy to extract informative graph knowledge in a self-supervised manner. Finally, we design a joint objective function of recommendation and contrastive learning to optimize the model. Extensive experiments demonstrate that RecipeRec outperforms state-of-the-art methods for recipe recommendation. Dataset and codes are available at https://github.com/meettyj/RecipeRec.

Learning effective recipe representations is essential in food studies. Unlike what has been developed for image-based recipe retrieval or learning structural text embeddings, the combined effect of multi-modal information (i.e., recipe images, text, and relation data) receives less attention. In this paper, we formalize the problem of multi-modal recipe representation learning to integrate the visual, textual, and relational information into recipe embeddings. In particular, we first present Large-RG, a new recipe graph data with over half a million nodes, making it the largest recipe graph to date. We then propose Recipe2Vec, a novel graph neural network based recipe embedding model to capture multi-modal information. Additionally, we introduce an adversarial attack strategy to ensure stable learning and improve performance. Finally, we design a joint objective function of node classification and adversarial learning to optimize the model. Extensive experiments demonstrate that Recipe2Vec outperforms state-of-the-art baselines on two classic food study tasks, i.e., cuisine category classification and region prediction. Dataset and codes are available at https://github.com/meettyj/Recipe2Vec.

Confirmation bias is a type of cognitive bias that involves seeking and prioritizing information that conforms to a pre-existing view or hypothesis that can negatively affect the decision-making process. We investigate the manifestation and mitigation of confirmation bias with an emphasis on the use of visualization. In a series of Amazon Mechanical Turk studies, participants selected evidence that supported or refuted a given hypothesis. We demonstrated the presence of confirmation bias and investigated the use of five simple visual representations, using color, positional, and length encodings for mitigating this bias. We found that at worst, visualization had no effect in the amount of confirmation bias present, and at best, it was successful in mitigating the bias. We discuss these results in light of factors that can complicate visual debiasing in non-experts.

It is well known that unhealthy food consumption plays a significant role in dietary and lifestyle-related diseases. Therefore, it is important for researchers to examine methods that may encourage the consumer to consider healthier dietary and lifestyle habits as diseases such as obesity, heart disease, and high blood pressure remain a worldwide issue. One promising approach to influencing healthy dietary and lifestyle habits is food recommendation models that recommend food to users based on various factors such as health effects, nutrition, preferences, and daily habits. Unfortunately, much of this work has focused on individual factors such as taste preferences and often neglects to understand other factors that influence our choices. Additionally, the evaluation of technological approaches often lacks user studies in the context of intended use. In this systematic review of food choice technology, we focus on the factors that may influence food choices and how technology can play a role in supporting those choices. We also describe existing work, approaches, trends, and issues in current food choice technology and give advice for future work areas in this space.

Health and well-being are integral parts of the human experience, and yet due to numerous factors are inaccessible for many communities. The transgender community is no exception and faces increased risk for both physical and mental illness. This population faces many unique challenges before, during, and after transition. To gain a deeper understanding of the trans community’s health and well-being needs, we conducted twenty-one interviews with transgender individuals to determine how they navigated their identities and transitions. From our interviews, we examine and highlight the unique needs of the trans population with respect to health, well-being, identity, and transition. We discuss how designers can better understand and accommodate the diversity of this community, give suggestions for the design of technologies for trans health and well-being, and contribute open areas of research.

Learning effective representations for recipes is essential in food studies for recommendation, classification, and other applications. Unlike what has been developed for learning textual or cross-modal embeddings for recipes, the structural relationship among recipes and food items are less explored. In this paper, we formalize the problem recipe representation learning with networks to involve both the textual feature and the structural relational feature into recipe representations. Specifically, we first present RecipeNet, a new and large-scale corpus of recipe data to facilitate network based food studies and recipe representation learning research. We then propose a novel heterogeneous recipe network embedding model, rn2vec, to learn recipe representations. The proposed model is able to capture textual, structural, and nutritional information through several neural network modules, including textual CNN, inner-ingredients transformer, and a graph neural network with hierarchical attention. We further design a combined objective function of node classification and link prediction to jointly optimize the model. The extensive experiments show that our model outperforms state-of-the-art baselines on two classic food study tasks. Dataset and codes are available at https://github.com/meettyj/rn2vec.

Review processes involve complex and often subjective decision-making tasks in which individual reviewers must read and rate submissions, such as a college application, along many relevant dimensions and typically with a rubric in mind. A common part of the work is committee review, where individual reviewers meet to discuss the merits of a particular submission in order to recommend an accept or reject decision. Prior work indicates that visualization and sensemaking support may be beneficial in such processes where reviewers must present the "story" of the applicant under question. We conducted a series of participatory design workshops with reviewers in the domain of holistic college admissions to better understand the challenges and opportunities regarding storytelling. Based on these workshops, we contribute a characterization for how reviewers in this domain construct visual stories, we provide guidance for designing for evidence capture and storytelling, and we draw parallels and distinctions between this domain and other reviewing domains.

"What design innovations can the ubiquity and features of mobile devices bring to the programming realm?" has been a long-standing topic of interest within the human-computer interaction community. Yet, the important design considerations for using mobile devices in programming contexts have not been analyzed systematically. Towards this goal, we review a sample of the existing research work on this topic and present a design space covering (i) the target contexts for the designed programming tools, (ii) the types of programming functionality supported, and (iii) the key design decisions made to support the programming functionality on the mobile devices. We also review the design processes in the existing work and discuss objectives for future research with respect to (i) the trade-offs in enabling programming support given the constraints of mobile devices and (ii) applying human-centered methods particularly in the design and evaluation of programming tools on mobile devices.

Various stakeholders in the sports domain rely on the analysis and presentation of sports data to derive insights. In particular, sportswriters construct game stories using statistical information; fans share their viewpoints based on the real-time stats while watching the game. In this paper, we explore how these stakeholders construct data-driven sports stories. We began by observing a sportswriter, then analyzed published sports stories, and characterized 1500 fan comments about particular sporting events. We found that their story needs were similar in some respects while quite different in others. Based on the findings, we implemented two exploratory prototypes: GameViews-Writers for sportswriters to quickly extract key game information and GameViews-Fans to support a real-time data-driven game-viewing experience for fans. We report insights from two user studies conducted with four professional sportswriters and eight sports fans, respectively. We discuss the results of these studies and present several avenues for future work.

Abstract Modern web technologies are enabling authors to create various forms of text visualization integration for storytelling. This integration may shape the stories' flow and thereby affect the reading experience. In this paper, we seek to understand two text visualization integration forms: (i) different text and visualization spatial arrangements (layout), namely, vertical and slideshow; and (ii) interactive linking of text and visualization (linking). Here, linking refers to a bidirectional interaction mode that explicitly highlights the explanatory visualization element when selecting narrative text and vice versa. Through a crowdsourced study with 180 participants, we measured the effect of layout and linking on the degree to which users engage with the story (user engagement), their understanding of the story content (comprehension), and their ability to recall the story information (recall). We found that participants performed significantly better in comprehension tasks with the slideshow layout. Participant recall was better with the slideshow layout under conditions with linking versus no linking. We also found that linking significantly increased user engagement. Additionally, linking and the slideshow layout were preferred by the participants. We also explored user reading behaviors with different conditions.

Online writers and journalism media are increasingly combining visualization (and other multimedia content) with narrative text to create narrative visualizations. Often, however, the two elements are presented independently of one another. We propose an approach to automatically integrate text and visualization elements. We begin with a writer»s narrative that presumably can be supported with visual data evidence. We leverage natural language processing, quantitative narrative analysis, and information visualization to (1) automatically extract narrative components (who, what, when, where) from data-rich stories, and (2) integrate the supporting data evidence with the text to develop a narrative visualization. We also employ bidirectional interaction from text to visualization and visualization to text to support reader exploration in both directions. We demonstrate the approach with a case study in the data-rich field of sports journalism.

Although initial ideas for building intuitive and usable handwriting applications originated nearly 30 years ago, recent advances in stylus technology and handwriting recognition are now making handwriting a viable text-entry option on touchscreen devices. In this paper, we use modern methods to replicate studies form the 80's to elicit hand-drawn gestures from users for common text-editing tasks in order to determine a "guessable' gesture set and to determine if the early results still apply given the ubiquity of touchscreen devices today. We analyzed 360 gestures, performed with either the finger or stylus, from 20 participants for 18 tasks on a modern tablet device. Our findings indicate that the mental model of "writing on paper' found in past literature largely holds even today, although today's users' mental model also appears to support manipulating the paper elements as opposed to annotating. In addition, users prefer using the stylus to finger touch for text editing, and we found that manipulating "white space' is complex. We present our findings as well as a stylus-based, user-defined gesture set for text editing.

Block-based programming languages are used by millions of people around the world. Blockly is a popular JavaScript library for creating visual block programming editors. To input a block, users employ a drag-and-drop input style. However, there are some limitations to this input style. We introduce a custom soft keyboard to input Blockly programs. This keyboard allows inputting, changing or editing blocks with a single touch. We evaluated the keyboard users' speed, number of touches, and errors while inputting a Blockly program and compared its performance with the drag-and-drop method. Our keyboard reduces the input errors by 68.37% and the keystrokes by 47.97 %. Moreover, it increases the input speed by 71.26% when compared to the drag-and-drop. The keyboard users perceived it to be physically less demanding with less effort than the drag-and-drop method. Moreover, participants rated the drag-and-drop method to have a higher frustration level. The Blockly keyboard was the preferred input method.

Holistic reviews are a common practice employed by universities in the USA to make admissions decisions. It is an individualized review process where reviewers assess an applicant's potential by considering various criteria including academic metrics, adversities faced, and personal attributes. While the factors considered in such reviews are broadly known, a detailed walk-through of the process is absent in existing literature. This is important to understand what is done in practice and to identify opportunities for technological interventions to support the complex and changing process. We employed cognitive task analysis and a socio-organizational approach to understand the holistic review process at a highly-selective, private university. We found the process to be nuanced and complex owing its complexity both to the numerous variables involved and the reviewers' thought processes. We present a rigorous, structured characterization of the review process and suggest possible leverage points for applying visualization decision-support tools.

Technology users are collecting data about themselves at an astounding rate. This explosion of data collection has not been matched by users' abilities to assimilate and apply this information. Visualization is a key means of bridging this gap, however, most approaches to visualization neglect individual differences, and focus instead on one-size-fits-all approaches. One proposed solution to this problem is adaptive visualization. To produce appropriate adaptive visualization tools, however, we must understand the relationship between a user's context and the visualization they require. In this paper, we present a study designed to understand the effects of visualizations that are mismatched, in terms of granularity, to user contexts. We show that users are able to interpret data visualizations most accurately and quickly when the information granularity of the visualization they are shown matches their need for detail and we discuss the consequences of mismatching the information granularity of a visualization to a user's information needs.

Supporting programming on touchscreen devices requires effective text input and editing methods. Unfortunately, the virtual keyboard can be inefficient and uses valuable screen space on already small devices. Recent advances in stylus input make handwriting a potentially viable text input solution for programming on touchscreen devices. The primary barrier, however, is that handwriting recognition systems are built to take advantage of the rules of natural language, not those of a programming language. In this paper, we explore this particular problem of handwriting recognition for source code. We collect and make publicly available a dataset of handwritten Python code samples from 15 participants and we characterize the typical recognition errors for this handwritten Python source code when using a state-of-the-art handwriting recognition tool. We present an approach to improve the recognition accuracy by augmenting a handwriting recognizer with the programming language grammar rules. Our experiment on the collected dataset shows an 8.6% word error rate and a 3.6% character error rate which outperforms standard handwriting recognition systems and compares favorably to typing source code on virtual keyboards.

When learning to use an Application Programming Interface (API), programmers need to understand the inputs and outputs (I/O) of the API functions. Current documentation tools automatically document the static information of I/O, such as parameter types and names. What is missing from these tools is dynamic information, such as I/O examples-actual valid values of inputs that produce certain outputs. In this paper, we demonstrate Docio, a prototype toolset we built to generate I/O examples. Docio logs I/O values when API functions are executed, for example in running test suites. Then, Docio puts I/O values into API documents as I/O examples. Docio has three programs: 1) funcWatch, which collects I/O values when API developers run test suites, 2) ioSelect, which selects one I/O example from a set of I/O values, and 3) ioPresent, which embeds the I/O examples into documents. In a preliminary evaluation, we used Docio to generate four hundred I/O examples for three C libraries: ffmpeg, libssh, and protobuf-c. Docio is open-source and available at: http://www3.nd.edu/~sjiang1/docio/.

Developers maintaining safety-critical systems need to assess the impact a proposed change would have upon existing safety controls. By leveraging the network of traceability links that are present in most safety-critical systems, we can push timely information about related hazards, environmental assumptions, and safety requirements to developers. In this work we take a design science approach to discover the informational needs of developers as they engage in software maintenance activities and then propose and evaluate techniques for presenting and visualizing this information. Through a human-centered study involving five safety-critical system practitioners and 14 experienced developers, we analyze the way in which developers use requirements knowledge while maintaining safety-critical code, identify their informational needs, and propose and evaluate a supporting visualization technique. The insights proposed as a result of this study can be used to design requirements-based knowledge tools for supporting developers' maintenance tasks.

The syntax-directed keyboard extension presented by Almusaly et al. in 2015 allows programmers to input Java source code with fewer errors and keystrokes compared to the soft QWERTY keyboard and it supports a comparable typing speed. While these results were obtained after only 10 minutes of practice, it is unclear how long term use affects performance. In this paper, we present an updated design for the original syntax-directed keyboard extension, replicate the original results, and evaluate the evolved design with Java programmers over eight sessions in a period of two weeks. Our results indicate that a programmer using the new keyboard extension for two weeks can input Java programs 16.5% faster (words per minute) than an expert QWERTY keyboard typist. In addition, we demonstrate that the efficiency and accuracy for inputting Java source code improves with repeated use over time and that perceived mental, physical, and temporal demands of the keyboard extension decrease over time.

A bad software development process leads to wasted effort and inferior products. In order to improve a software process, it must be first understood. Our unique approach in this paper uses code and test changes to understand conformance to the Test Driven Development (TDD) process. We designed and implemented TDDViz , a tool that supports developers in better understanding how they conform to TDD. TDDViz supports this understanding by providing novel visualizations of developers’ TDD process. To enable TDDViz ’s visualizations, we developed a novel automatic inferencer that identifies the phases that make up the TDD process solely based on code and test changes. We evaluate TDDViz using two complementary methods: a controlled experiment with 35 participants to evaluate the visualization, and a case study with 2601 TDD Sessions to evaluate the inference algorithm. The controlled experiment shows that, in comparison to existing visualizations, participants performed significantly better when using TDDViz to answer questions about code evolution. In addition, the case study shows that the inferencing algorithm in TDDViz infers TDD phases with an accuracy (F-measure) of 87%.

As touchscreen mobile devices grow in popularity, it is inevitable that software developers will eventually want to write code on them. However, writing code on a soft (or virtual) keyboard is cumbersome due to the device size and lack of tactile feedback. We present a soft syntax-directed keyboard extension to the QWERTY keyboard for Java program input on touchscreen devices and evaluate this keyboard with Java programmers. Our results indicate that a programmer using the keyboard extension can input a Java program with fewer errors and using fewer keystrokes per character than when using a standard soft keyboard alone. In addition, programmers maintain an overall typing speed in words per minute that is equivalent to that on the standard soft keyboard alone. The keyboard extension was shown to be mentally, physically, and temporally less demanding than the standard soft keyboard alone when inputting a Java program.

Researchers in AI and Operations Research employ the framework of Markov Decision Processes (MDPs) to formalize problems of sequential decision making under uncertainty. A common approach is to implement a simulator of the stochastic dynamics of the MDP and a Monte Carlo optimization algorithm that invokes this simulator to solve the MDP. The resulting software system is often realized by integrating several systems and functions that are collectively subject to failures of specification, implementation, integration, and optimization. We present these failures as queries for a computational steering visual analytic system (MDPVIS). MDPVIS addresses three visualization research gaps. First, the data acquisition gap is addressed through a general simulator-visualization interface. Second, the data analysis gap is addressed through a generalized MDP information visualization. Finally, the cognition gap is addressed by exposing model components to the user. MDPVIS generalizes a visualization for wildfire management. We use that problem to illustrate MDPVIS.

Markov Decision Process (MDP) simulators and optimization algorithms integrate several systems and functions that are collectively subject to failures of specification, implementation, integration, and optimization. We present a domain agnostic visual analytic design and implementation for testing and debugging MDPs: MDPVIS.

Information visualization construction tools generally tend to fall in one of two disparate categories. Either they offer simple but inflexible visualization templates, or else they offer low-level graphical primitives which need to be assembled manually. Those that do offer flexible, domain-specific abstractions rarely focus on incrementally building and transforming visualizations, which could reduce limitations on the style of workflows supported. We present a Haskell-embedded DSL for data visualization that is designed to provide such abstractions and transformations. This DSL achieves additional expressiveness and flexibility through common functional programming idioms and the Haskell type class hierarchy.

Exploring large network datasets, such as scientific collaboration networks, is challenging because they often contain a large number of nodes and edges in several types and with multiple attributes. Analyses of such networks are often long and complex, and may require several sessions by multiple users. Therefore, it is often difficult for users to recall their own exploration history or share it with others. We introduce GraphTrail, an interactive visualization for analyzing networks through exploration of node and edge aggregates that captures users' interactions and integrates this history directly in the exploration workspace. To facilitate large network analysis, GraphTrail integrates aggregation with familiar charts, drag-and-drop interaction on a canvas, and a novel pivoting mechanism for transitioning between aggregates. Through a three-month field study with a team of archeologists and a qualitative lab study with ten users, we demonstrate the effectiveness of our design and the benefits of integrated exploration history, including analysis comprehension, insight discovery, and exploration recall.

Tools exist for people to create visualizations with their data; however, they are often designed for programmers or they restrict less technical people to pre-defined templates. This can make creating novel, custom visualizations difficult for the average person. For example, existing tools typically do not support syntax or interaction techniques that are natural to end users. To explore how to support a more natural production of data visualizations by end users, we conducted an exploratory study to illuminate the structure and content of the language employed by end users when describing data visualizations. We present our findings from the study and discuss their design implications for future visualization languages and toolkits.

The recent emphasis on sustainability has made consumers more aware of their responsibility for saving resources, in particular, electricity. Consumers can better understand how to save electricity by gaining awareness of their consumption beyond the typical monthly bill. We conducted a study to understand consumers' awareness of energy consumption in the home and to determine their requirements for an interactive, always-on interface for exploring data to gain awareness of home energy consumption. In this paper, we describe a three-stage approach to supporting electricity conservation routines: raise awareness, inform complex changes, and maintain sustainable routines. We then present the findings from our study to support design implications for energy consumption feedback interfaces.

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.

Communicating dynamic motion content, such as exercise, with a static medium, such as paper, is difficult. The technology exists for presenting 3D animated exercise content to patients, however, the tools for allowing exercise domain experts to effectively author the content do not exist. We conducted two formative studies with exercise science domain experts to discover the requirements for an exercise prescription authoring notation. Based on our findings, we implemented a software prototype and performed a think-aloud study to understand its strengths and weaknesses. The results of our studies have implications for any software solution aimed at the authoring of physical activity content.

We present a psychology-inspired approach for generating a character' s anticipation of and response to an impending head or upper body impact. Protective anticipatory movement is built upon several actions that have been identified in the psychology literature as response mechanisms in monkeys and in humans. These actions are parameterized by a model of the approaching object (the threat) and are defined as procedural rules. We present a hybrid forward and inverse kinematic blending technique to guide the character to the pose that results from these rules while maintaining properties of a balanced posture as well as characteristics of the behavior just prior to the interaction. In our case, these characteristics are determined by a motion capture sequence. We combine our anticipation model with a physically-based dynamic response to produce animations where a character anticipates an impact before collision and reacts to the contact, physically, after the collision. We present a variety of examples including threats that vary in approach direction, size and speed.

Automatically generated anticipation is a largely overlooked component of response in character motion for computer animation. We present an approach for generating anticipation to unexpected interactions with examples taken from human motion capture data. Our system generates animation by quickly selecting an anticipatory action using a Support Vector Machine (SVM) which is trained offline to distinguish the characteristics of a given scenario according to a metric that assesses predicted damage and energy expenditure for the character. We show our results for a character that can anticipate by blocking or dodging a threat coming from a variety of locations and targeting any part of the body, from head to toe.

To present an accurate and compelling view of a new environment, architectural and urban planning applications both require animations of people. Ideally, these animations would be easy for a non-programmer to construct, just as buildings and streets can be modeled by an architect or artist using commercial modeling software. In this paper we explore an approach for generating reactive path following based on the user's examples of the desired behavior. The examples are used to build a model of the desired reactive behavior. The model is combined with reactive control methods to produce natural 2D pedestrian trajectories. The system then automatically generates 3D pedestrian locomotion using motion capture resequencing algorithms. We discuss the accuracy of the model of pedestrian motion and show that simple direction primitives can be recorded and used to build natural, reactive, path-following behaviors.