Web Search, Web Tutorials & Software Applications: Characterizing and Supporting the Coordinated Use of Online Resources for Performing Work in Feature-Rich Software

Web search and other online resources serve an integral role in how people learn and use feature-rich software (e.g., Adobe Photoshop) on a daily basis. Users depend on web resources both as a first line of technical support, and as a means for coping with system complexity. For example, people rely on web resources to learn new tasks, to troubleshoot problems, or to remind themselves of key task details. When users rely on web resources to support their work, their interactions are distributed over three user environments: (1) the search engine, (2) retrieved documents, and (3) the application's user interface. As users interact with these environments, their actions generate a rich set of signals that characterize how the population thinks about and uses software systems "in the wild," on a day-to-day basis. This dissertation presents three works that successively connect and associate signals and artifacts across these environments, thereby generating novel insights about users and their tasks, and enabling powerful new end-user tools and services. These three projects are as follows: Characterizing usability through search (CUTS): The CUTS system demonstrates that aggregate logs of web search queries can be leveraged to identify common tasks and potential usability problems faced by the users of any publicly available interactive system. For example, in 2011 I examined query data for the Firefox web browser. Automated analysis uncovered approximately 150 variations of the query "Firefox how to get the menu bar back", with queries issued once every 32 minutes on average. Notably, this analysis did not depend on direct access to query logs. Instead, query suggestions services and online advertising valuations were leveraged to approximate aggregate query data. Nevertheless, these data proved to be timely, to have a high degree of ecological validity, and to be arguably less prone to self-selection bias than data gathered via traditional usability methods. Query-feature graphs (QF-Graphs): Query-feature graphs are structures that map high-level descriptions of a user's goals to the specific features and commands relevant to achieving those goals in software. QF-graphs address an important instance of the more general vocabulary mismatch problem. For example, users of the GIMP photo manipulation software often want to "make a picture black and white", and fail to recognize the relevance of the applicable commands, which include: "desaturate", and "channel mixer". The key insights for building QF-graphs are that: (1) queries concisely express the user's goal in the user's own words, and (2) retrieved tutorials likely include both query terms, as well as terminology from the application's interface (e.g., the names of commands). QF-graphs are generated by mining these co-occurrences across thousands of query-tutorial pairings. InterTwine: InterTwine explores interaction possibilities that arise when software applications, web search, and online support materials are directly integrated into a single productivity system. With InterTwine, actions in the web browser directly impact how information is presented in a software application, and vice versa. For example, when a user opens a web tutorial in their browser, the application's menus and tooltips are updated to highlight the commands mentioned therein. These embellishments are designed to help users orient themselves after switching between the web browser and the application. InterTwine also augments web search results to include details of past application use. Search snippets gain before and after pictures and other metadata detailing how the user's personal work document evolved the last time they visited the page. This feature was motivated by the observation that existing mechanisms (e.g., highlighting visited links) are often insufficient for recalling which resources were previously helpful vs. unhelpful for accomplishing a task. Finally, the dissertation concludes with a discussion of the advantages, limitations and challenges of this research, and presents an outline for future work

Design and Evaluation of a Presentation Maestro: Controlling Electronic Presentations Through Gesture

Gesture-based interaction has long been seen as a natural means of input for electronic presentation systems; however, gesture-based presentation systems have not been evaluated in real-world contexts, and the implications of this interaction modality are not known. This thesis describes the design and evaluation of Maestro, a gesture-based presentation system which was developed to explore these issues. This work is presented in two parts. The first part describes Maestro's design, which was informed by a small observational study of people giving talks; and Maestro's evaluation, which involved a two week field study where Maestro was used for lecturing to a class of approximately 100 students. The observational study revealed that presenters regularly gesture towards the content of their slides. As such, Maestro supports several gestures which operate directly on slide content (e.g., pointing to a bullet causes it to be highlighted). The field study confirmed that audience members value these content-centric gestures. Conversely, the use of gestures for navigating slides is perceived to be less efficient than the use of a remote. Additionally, gestural input was found to result in a number of unexpected side effects which may hamper the presenter's ability to fully engage the audience. The second part of the thesis presents a gesture recognizer based on discrete hidden Markov models (DHMMs). Here, the contributions lie in presenting a feature set and a factorization of the standard DHMM observation distribution, which allows modeling of a wide range of gestures (e.g., both one-handed and bimanual gestures), but which uses few modeling parameters. To establish the overall robustness and accuracy of the recognition system, five new users and one expert were asked to perform ten instances of each gesture. The system accurately recognized 85% of gestures for new users, increasing to 96% for the expert user. In both cases, false positives accounted for fewer than 4% of all detections. These error rates compare favourably to those of similar systems

Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs

To design effective vaccine policies, policymakers need detailed data about who has been vaccinated, who is holding out, and why. However, existing data in the US are insufficient: reported vaccination rates are often delayed or missing, and surveys of vaccine hesitancy are limited by high-level questions and self-report biases. Here, we show how large-scale search engine logs and machine learning can be leveraged to fill these gaps and provide novel insights about vaccine intentions and behaviors. First, we develop a vaccine intent classifier that can accurately detect when a user is seeking the COVID-19 vaccine on search. Our classifier demonstrates strong agreement with CDC vaccination rates, with correlations above 0.86, and estimates vaccine intent rates to the level of ZIP codes in real time, allowing us to pinpoint more granular trends in vaccine seeking across regions, demographics, and time. To investigate vaccine hesitancy, we use our classifier to identify two groups, vaccine early adopters and vaccine holdouts. We find that holdouts, compared to early adopters matched on covariates, are 69% more likely to click on untrusted news sites. Furthermore, we organize 25,000 vaccine-related URLs into a hierarchical ontology of vaccine concerns, and we find that holdouts are far more concerned about vaccine requirements, vaccine development and approval, and vaccine myths, and even within holdouts, concerns vary significantly across demographic groups. Finally, we explore the temporal dynamics of vaccine concerns and vaccine seeking, and find that key indicators emerge when individuals convert from holding out to preparing to accept the vaccine

Characterizing Search Behavior in Productivity Software

Complex software applications expose hundreds of commands to users through intricate menu hierarchies. One of the most popular productivity software suites, Microsoft Office, has recently developed functionality that allows users to issue free-form text queries to a search system to quickly find commands they want to execute, retrieve help documentation or access web results in a unified interface. In this paper, we analyze millions of search sessions originating from within Microsoft Office applications, collected over one month of activity, in an effort to characterize search behavior in productivity software. Our research brings together previous efforts in analyzing command usage in large-scale applications and efforts in understanding search behavior in environments other than the web. Our findings show that users engage primarily in command search, and that re-accessing commands through search is a frequent behavior. Our work represents the first large-scale analysis of search over command spaces and is an important first step in understanding how search systems integrated with productivity software can be successfully developed

Opportunities and Challenges Around a Tool for Social and Public Web Activity Tracking

While the web contains many social websites, people are generally left in the dark about the activities of other people traversing the web as a whole. In this paper, we explore the potential benefits and privacy considerations around generating a real-time, publicly accessible stream of web activity where users can publish chosen parts of their web browsing data. Taking inspiration from social media systems, we describe individual benefits that can be unlocked by such sharing and that may incentivize users to publish aspects of their browsing. We ask whether and how these benefits outweigh potential costs in lost privacy. We conduct our study of public web activity sharing through scenario-based interviews and a field deployment of a tool for web activity sharing

When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming

AI powered code-recommendation systems, such as Copilot and CodeWhisperer, provide code suggestions inside a programmer's environment (e.g., an IDE) with the aim to improve their productivity. Since, in these scenarios, programmers accept and reject suggestions, ideally, such a system should use this feedback in furtherance of this goal. In this work we leverage prior data of programmers interacting with Copilot to develop interventions that can save programmer time. We propose a utility theory framework, which models this interaction with programmers and decides when and which suggestions to display. Our framework Conditional suggestion Display from Human Feedback (CDHF) is based on predictive models of programmer actions. Using data from 535 programmers we build models that predict the likelihood of suggestion acceptance. In a retrospective evaluation on real-world programming tasks solved with AI-assisted programming, we find that CDHF can achieve favorable tradeoffs. Our findings show the promise of integrating human feedback to improve interaction with large language models in scenarios such as programming and possibly writing tasks.Comment: arXiv admin note: text overlap with arXiv:2210.1430

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming

Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To make progress, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed CUPS, a taxonomy of common programmer activities when interacting with Copilot. Our study of 21 programmers, who completed coding tasks and retrospectively labeled their sessions with CUPS, showed that CUPS can help us understand how programmers interact with code-recommendation systems, revealing inefficiencies and time costs. Our insights reveal how programmers interact with Copilot and motivate new interface designs and metrics