Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.7k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query- by-example search over UIs.

To evaluate the performance of mobile app designs, designers and researchers employ techniques such as A/B, usability, and analytics-driven testing. While these are all useful strategies for evaluating known designs, comparing many divergent solutions to identify the most performant remains a costly and difficult problem. This paper introduces a design performance testing approach that leverages existing app implementations and crowd workers to enable comparative testing at scale. This approach is manifest in ZIPT, a zero-integration performance testing platform that allows designers to collect detailed design and interaction data over any Android app -- including apps they do not own and did not build. Designers can deploy scripted tests via ZIPT to collect aggregate user performance metrics (e.g., completion rate, time on task) and qualitative feedback over third-party apps. Through case studies, we demonstrate that designers can use ZIPT's aggregate data and visualizations to understand the relative performance of interaction patterns found in the wild, and identify usability issues in existing Android apps.

Ranking users in social networks is a well-studied problem, typically solved by algorithms that leverage network structure to identify influential users and recommend people to follow. In the last decade, however, curation --- users sharing and promoting content in a network --- has become a central social activity, as platforms like Facebook, Twitter, Pinterest, and GitHub drive growth and engagement by connecting users through content and content to users. While existing algorithms reward users that are highly active with higher rankings, they fail to account for users' curatorial taste. This paper introduces CuRank, an algorithm for ranking users and content in social networks by explicitly modeling three characteristics of a good curator: discerning taste, high activity, and timeliness. We evaluate CuRank on datasets from two popular social networks --- GitHub and Vine --- and demonstrate its efficacy at ranking content and identifying good curators.

Design plays an important role in adoption of apps. App design, however, is a complex process with multiple design activities. To enable data-driven app design applications, we present interaction mining -- capturing both static (UI layouts, visual details) and dynamic (user flows, motion details) components of an app's design. We present ERICA, a system that takes a scalable, human-computer approach to interaction mining existing Android apps without the need to modify them in any way. As users interact with apps through ERICA, it detects UI changes, seamlessly records multiple data-streams in the background, and unifies them into a user interaction trace. Using ERICA we collected interaction traces from over a thousand popular Android apps. Leveraging this trace data, we built machine learning classifiers to detect elements and layouts indicative of 23 common user flows. User flows are an important component of UX design and consists of a sequence of UI states that represent semantically meaningful tasks such as searching or composing. With these classifiers, we identified and indexed more than 3000 flow examples, and released the largest online search engine of user flows in Android apps.

Time series shapelet discovery algorithm finds subsequences from a set of time series for use as primitives for time series classification. This algorithm has drawn a lot of interest because of the interpretability of its results. However, computation requirements restrict the algorithm from dealing with large data sets and may limit its application in many domains. In this paper, we address this issue by redesigning the algorithm for implementation on highly parallel Graphics Process Units (GPUs). We investigate several concepts of GPU programming and propose a dynamic programming algorithm that is suitable for implementation on GPUs. Results show that the proposed GPU implementation significantly reduces the running time of the shapelet discovery algorithm. For example, on the largest sample dataset from the original authors, the running time is reduced from half a day to two minutes.