Accessibility checkers are tools in support of accessible app development, and their use is encouraged by accessibility best practices. However, most current checkers evaluate static or mechanically-generated contexts, failing to capture common accessibility errors impacting mobile app functionality. In this work, we define functiona11ity errors as accessibility barriers that only manifest through interaction (i.e., named according to a blend of "functionality" and "accessibility"). We introduce TaskAudit, which comprises three components: a Task Generator that constructs interactive tasks from app screens, a Task Executor that uses agents with a screen reader proxy to perform these tasks, and an Accessibility Analyzer that detects and reports accessibility errors by examining interaction traces. Our evaluation on real-world apps shows that TaskAudit detects 48 functiona11ity errors from 54 app screens, compared to between 4 and 20 with existing checkers. Our analysis demonstrates common error patterns that TaskAudit can detect in addition to those from prior work, including label-functionality mismatch, cluttered navigation, and inappropriate feedback.

As LLM-based computer-use agents (CUAs) begin to autonomously interact with real-world interfaces, understanding their vulnerability to manipulative interface designs becomes increasingly critical. We introduce SusBench, an online benchmark for evaluating the susceptibility of CUAs to UI dark patterns, designs that aim to manipulate or deceive users into taking unintentional actions. Drawing nine common dark pattern types from existing taxonomies, we developed a method for constructing believable dark patterns on real-world consumer websites through code injections, and designed 313 evaluation tasks across 55 websites. Our study with 29 participants showed that humans perceived our dark pattern injections to be highly realistic, with the vast majority of participants not noticing that these had been injected by the research team. We evaluated five state-of-the-art CUAs on the benchmark. We found that both human participants and agents are particularly susceptible to the dark patterns of Preselection, Trick Wording, and Hidden Information, while being resilient to other overt dark patterns. Our findings inform the development of more trustworthy CUAs, their use as potential human proxies in evaluating deceptive designs, and the regulation of an online environment increasingly navigated by autonomous agents.

Many mobile apps are inaccessible, thereby excluding people from their potential benefits. Existing rule-based accessibility checkers aim to mitigate these failures by identifying errors early during development but are constrained in the types of errors they can detect. We present ScreenAudit, an LLM-powered system designed to traverse mobile app screens, extract metadata and transcripts, and identify screen reader accessibility errors overlooked by existing checkers. We recruited six accessibility experts including one screen reader user to evaluate ScreenAudit's reports across 14 unique app screens. Our findings indicate that ScreenAudit achieves an average coverage of 69.2%, compared to only 31.3% with a widely-used accessibility checker. Expert feedback indicated that ScreenAudit delivered higher-quality feedback and addressed more aspects of screen reader accessibility compared to existing checkers, and that ScreenAudit would benefit app developers in real-world settings.

Despite significant progress in the capabilities of mobile devices and applications, most apps remain oblivious to their users' abilities. To enable apps to respond to users' situated abilities, we created the Ability-Based Design Mobile Toolkit (ABD-MT). ABD-MT integrates with an app's user input and sensors to observe a user's touches, gestures, physical activities, and attention at runtime, to measure and model these abilities, and to adapt interfaces accordingly. Conceptually, ABD-MT enables developers to engage with a user's "ability profile,'' which is built up over time and inspectable through our API. As validation, we created example apps to demonstrate ABD-MT, enabling ability-aware functionality in 91.5% fewer lines of code compared to not using our toolkit. Further, in a study with 11 Android developers, we showed that ABD-MT is easy to learn and use, is welcomed for future use, and is applicable to a variety of end-user scenarios.

Measures of human performance for touch-based systems have focused mainly on overall metrics like touch accuracy and target acquisition speed. But touches are not atomic—they unfold over time and space, especially for users with limited fine motor function, for whom it can be difficult to perform quick, accurate touches. To gain insight into what happens during a touch, we offer 15 target-agnostic touch metrics, most of which have not been mathematically formalized in the literature. They are touch direction, variability, drift, duration, extent, absolute/signed area change, area variability, area deviation, area extent, absolute/signed angle change, angle variability, angle deviation, and angle extent. These metrics regard a touch as a time series of ovals instead of a mere (x, y) coordinate. We provide mathematical definitions and visual depictions of our metrics, and consider policies for calculating our metrics when multiple fingers perform coincident touches. To exercise our metrics, we collected touch data from 27 participants, 15 of whom reported having limited fine motor function. Our results show that our metrics effectively characterize touch behaviors including fine-motor challenges. Our metrics can be useful for both understanding users and for evaluating touch-based systems to inform their design.

We present the first large-scale longitudinal analysis of missing label accessibility failures in Android apps. We developed a crawler and collected monthly snapshots of 312 apps over 16 months. We use this unique dataset in empirical examinations of accessibility not possible in prior datasets. Key large-scale findings include missing label failures in 55.6% of unique image-based elements, longitudinal improvement in ImageButton elements but not in more prevalent ImageView elements, that 8.8% of unique screens are unreachable without navigating at least one missing label failure, that app failure rate does not improve with number of downloads, and that effective labeling is neither limited to nor guaranteed by large software organizations. We then examine longitudinal data in individual apps, presenting illustrative examples of accessibility impacts of systematic improvements, incomplete improvements, interface redesigns, and accessibility regressions. We discuss these findings and potential opportunities for tools and practices to improve label-based accessibility.

Animated GIF images have become prevalent in internet culture, often used to express richer and more nuanced meanings than static images. But animated GIFs often lack adequate alternative text descriptions, and it is challenging to generate such descriptions automatically, resulting in inaccessible GIFs for blind or low-vision (BLV) users. To improve the accessibility of animated GIFs for BLV users, we provide a system called Ga11y (pronounced “galley”), for creating GIF annotations. Ga11y combines the power of machine intelligence and crowdsourcing and has three components: an Android client for submitting annotation requests, a backend server and database, and a web interface where volunteers can respond to annotation requests. We evaluated three human annotation interfaces and employ the one that yielded the best annotation quality. We also conducted a multi-stage evaluation with 12 BLV participants from the United States and China, receiving positive feedback.

Current performance measures with touch-based systems usually focus on overall performance, such as touch accuracy and target acquisition speed. But a touch is not an atomic event; it is a process that unfolds over time, and this process can be characterized to gain insight into users’ touch behaviors. To this end, our work proposes 13 target-agnostic touch performance metrics to characterize what happens during a touch. These metrics are: touch direction, variability, drift, duration, extent, absolute/signed area change, area variability, area deviation, absolute/signed angle change, angle variability, and angle deviation. Unlike traditional touch performance measures that treat a touch as a single (x, y) coordinate, we regard a touch as a time series of ovals that occur from finger-down to finger-up. We provide a mathematical formula and intuitive description for each metric we propose. To evaluate our metrics, we run an analysis on a publicly available dataset containing touch inputs by people with and without limited fine motor function, finding our metrics helpful in characterizing different fine motor control challenges. Our metrics can be useful to designers and evaluators of touch-based systems, particularly when making touch screens accessible to all forms of touch.

User interface design is a complex task that involves designers examining a wide range of options. We present Spacewalker, a tool that allows designers to rapidly search a large design space for an optimal web UI with integrated support. Designers first annotate each attribute they want to explore in a typical HTML page, using a simple markup extension we designed. Spacewalker then parses the annotated HTML specification, and intelligently generates and distributes various configurations of the web UI to crowd workers for evaluation. We enhanced a genetic algorithm to accommodate crowd worker responses from pairwise comparison of UI designs, which is crucial for obtaining reliable feedback. Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.

Wake-up-free techniques (e.g., Raise-to-Speak) are important for improving the voice input experience. We present ProxiMic, a close-to-mic (within 5 cm) speech sensing technique using only one microphone. With ProxiMic, a user keeps a microphone-embedded device close to the mouth and speaks directly to the device without wake-up phrases or button presses. To detect close-to-mic speech, we use the feature from pop noise observed when a user speaks and blows air onto the microphone. Sound input is first passed through a low-pass adaptive threshold filter, then analyzed by a CNN which detects subtle close-to-mic features (mainly pop noise). Our two-stage algorithm can achieve 94.1% activation recall, 12.3 False Accepts per Week per User (FAWU) with 68 KB memory size, which can run at 352 fps on the smartphone. The user study shows that ProxiMic is efficient, user-friendly, and practical.

We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web. HelpViz transforms text instructions to graphical tutorials in batch, by extracting a sequence of actions from each text instruction through an instruction parsing model, and executing the extracted actions on a simulation infrastructure that manages an array of Android emulators. The automatic execution of each instruction produces a set of graphical and structural assets, including images, videos, and metadata such as clicked elements for each step. HelpViz then synthesizes a tutorial by combining parsed text instructions with the generated assets, and contextualizes the tutorial to user interaction by tracking the user’s progress and highlighting the next step. Our experiments with HelpViz indicate that our pipeline improved tutorial execution robustness and that participants preferred tutorials generated by HelpViz over text-based instructions. HelpViz promises a cost-effective approach for generating contextual visual tutorials for mobile interaction at scale.

We present ForceBoard, a pressure-based input technique that enables text entry by subtle finger motion. To enter text, users apply pressure to control a multi-letter-wide sliding cursor on a one-dimensional keyboard with alphabetical ordering, and confirm the selection with a quick release. We examined the error model of pressure control for successive and error-tolerant input, which was incorporated into a Bayesian algorithm to infer user input. A user study showed that, after a 10-minute training, the average text entry rate reached 4.2 wpm (Words Per Minute) for character-level input, and 11.0 wpm for word-level input. Users reported that ForceBoard was easy to learn and interesting to use. These results demonstrated the feasibility of applying pressure as the main channel for text entry. We conclude by discussing the limitation, as well as the potential of ForceBoard to support interaction with constraints from form factor, social concern and physical environments.

We present 1D Handwriting, a unistroke gesture technique enabling text entry on a one-dimensional interface. The challenge is to map two-dimensional handwriting to a reduced one-dimensional space, while achieving a balance between memorability and performance efficiency. After an iterative design, we finally derive a set of ambiguous two-length unistroke gestures, each mapping to 1-4 letters. To input words, we design a Bayesian algorithm that takes into account the probability of gestures and the language model. To input letters, we design a pause gesture allowing users to switch into letter selection mode seamlessly. Users studies show that 1D Handwriting significantly outperforms a selection-based technique (a variation of 1Line Keyboard) for both letter input (4.67 WPM vs. 4.20 WPM) and word input (9.72 WPM vs. 8.10 WPM). With extensive training, text entry rate can reach 19.6 WPM. Users' subjective feedback indicates 1D Handwriting is easy to learn and efficient to use. Moreover, it has several potential applications for other one-dimensional constrained interfaces.