development

Users increasingly interact with a heterogeneous collection of computing devices. The applications that users employ on those devices, however, still largely provide user experiences that assume the use of a single computer. This failure is due in part to the difficulty of creating user experiences that span multiple devices, particularly the need to manage identifying, connecting to, and communicating with other devices. In this paper we present an infrastructure based on instant messaging that simplifies adding that additional functionality to applications. Our infrastructure elevates device ownership to a first class property, allowing developers to provide functionality that spans personal devices without writing code to manage users' devices or establish connections among them. It also provides simple mechanisms for applications to send information, events, or commands between a user's devices. We demonstrate the effectiveness of our infrastructure by presenting a set of sample applications built with it and a user study demonstrating that developers new to the infrastructure can implement all of the cross-device functionality for three applications in, on average, less than two and a half hours.

We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we explicitly support both code and data. Gestalt allows developers to implement a classification pipeline, analyze data as it moves through that pipeline, and easily transition between implementation and analysis. An experiment shows this significantly improves the ability of developers to find and fix bugs in machine learning systems. Our discussion of Gestalt and our experimental observations provide new insight into general-purpose support for the machine learning process.