Mental health issues among college students have reached critical levels, significantly impacting academic performance and overall wellbeing. Predicting and understanding mental health status among college students is challenging due to three main factors: the necessity for large-scale longitudinal datasets, the prevalence of black-box machine learning models lacking transparency, and the tendency of existing approaches to provide aggregated insights at the population level rather than individualized understanding. To tackle these challenges, this paper presents I-HOPE, the first Interpretable Hierarchical mOdel for Personalized mEntal health prediction. I-HOPE is a two-stage hierarchical model that connects raw behavioral features to mental health status through five defined behavioral categories as interaction labels. We evaluate I-HOPE on the College Experience Study, the longest longitudinal mobile sensing dataset. This dataset spans five years and captures data from both pre-pandemic periods and the COVID-19 pandemic. I-HOPE achieves a prediction accuracy of 91%, significantly surpassing the 60-70% accuracy of baseline methods. In addition, I-HOPE distills complex patterns into interpretable and individualized insights, enabling the future development of tailored interventions and improving mental health support.

The field of affective computing focuses on recognizing, interpreting, and responding to human emotions, and has broad applications across education, child development, and human health and wellness. However, developing affective computing pipelines remains labor-intensive due to the lack of software frameworks that support multimodal, multi-domain emotion recognition applications. This often results in redundant effort when building pipelines for different applications. While recent frameworks attempt to address these challenges, they remain limited in reducing manual effort and ensuring cross-domain generalizability. We introduce AffectEval, a modular and customizable framework to facilitate the development of affective computing pipelines while reducing the manual effort and duplicate work involved in developing such pipelines. We validate AffectEval by replicating prior affective computing experiments, and we demonstrate that our framework reduces programming effort by up to 90%, as measured by the reduction in raw lines of code.

The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been linked to negative outcomes such as addiction and regret, significantly impacting well-being. Leveraging the longest longitudinal dataset collected over four college years through passive mobile sensing, this study is the first to examine the relationship between students’ smartphone unlocking behaviors and their mental health at scale in real-world settings. We provide the first evidence demonstrating the predictability of phone unlocking behaviors for mental health outcomes based on a large dataset, highlighting the potential of these novel features for future predictive models. Our findings reveal important variations in smartphone usage across genders and locations, offering a deeper understanding of the interplay between digital behaviors and mental health. We highlight future research directions aimed at mitigating adverse effects and promoting digital well-being in this population.

The prevalence of social media and its escalating impact on mental health has highlighted the need for effective digital wellbeing strategies. Current digital wellbeing interventions have primarily focused on reducing screen time and social media use, often neglecting the potential benefits of these platforms. This paper introduces a new perspective centered around empowering positive social media experiences, instead of limiting users with restrictive rules. In line with this perspective, we lay out the key requirements that should be considered in future work, aiming to spark a dialogue in this emerging area. We further present our initial effort to address these requirements with PauseNow, an innovative digital wellbeing intervention designed to align users’ digital behaviors with their intentions. PauseNow leverages digital nudging and intention-aware recommendations to gently guide users back to their original intentions when they “get lost” during their digital usage, promoting a more mindful use of social media.

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.

Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced automated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust's classifiers outperform the state of the art.

Prefetching and caching is a fundamental approach to reduce user-perceived latency, and has been shown effective in various domains for decades. However, its application on today’s mobile apps remains largely under-explored. This is an important but overlooked research area since mobile devices have become the dominant platform, and this trend is reflected in the billions of mobile devices and millions of mobile apps in use today. At the same time, user-perceived latency has been shown to have a large impact on mobile-user experience and can cause significant economic consequences. ❧ In this dissertation, I aim to fill this gap by providing a multifaceted solution to establish the foundation for exploring prefetching and caching in the mobile-app domain. To that end, my dissertation consists of four major elements. As a first step, I conducted an extensive study to investigate the opportunities for applying prefetching and caching techniques in mobile apps, providing empirical evidence on their applicability and demonstrating insights to guide future techniques. Second, I developed PALOMA, the first content-based prefetching technique for mobile apps using program analysis, which has achieved significant latency reduction with high accuracy and negligible overhead. Third, I constructed HiPHarness, a tailorable framework for investigating history-based prefetching in a wide range of scenarios. Guided by today’s stringent privacy regulations that have limited the access to mobile-user data, I further leveraged HiPHarness to conduct the first study on history-based prefetching with “small” prediction models, demonstrating its feasibility on mobile platforms and in turn, opening up a new research direction. Finally, to reduce the manual effort required in evaluating prefetching and caching techniques, I have devised FrUITeR, a customizable framework for assessing test-reuse techniques, in order to automatically select suitable test cases for evaluating prefetching and caching techniques without real users’ engagement as required previously.

UI testing is tedious and time-consuming due to the manual effort required. Recent research has explored opportunities for reusing existing UI tests from an app to automatically generate new tests for other apps. However, the evaluation of such techniques currently remains manual, unscalable, and unreproducible, which can waste effort and impede progress in this emerging area. We introduce FrUITeR, a framework that automatically evaluates UI test reuse in a reproducible way. We apply FrUITeR to existing test-reuse techniques on a uniform benchmark we established, resulting in 11,917 test reuse cases from 20 apps. We report several key findings aimed at improving UI test reuse that are missed by existing work.

A large number of mobile-app analysis and instrumentation techniques have emerged in the past decade. However, those techniques’ components are difficult to extract and reuse outside their original tools, their evaluation results are hard to reproduce, and the tools themselves are hard to compare. This paper introduces DECREE, an infrastructure intended to guide such techniques to be reproducible, practical, reusable, and easy to adopt in practice. DECREE allows researchers and developers to easily discover existing solutions to their needs, enables unbiased and reproducible evaluation, and supports easy construction and execution of replication studies. The paper describes DECREE's three modules and its potential to fundamentally alter how research is conducted in this area.

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address “what” and “when” to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applications.