Many disciplines, including health science, increasingly demand custom applications that synthesize cloud, mobile and web functionality. But creating even simple apps is difficult. Why? In this paper, guided by Cognitive Dimensions, we explore the design space for relevant programming notations and supporting tools, and we pinpoint what we hypothesize to be specific obstacles in the creation of cloud-mobile-web apps. Among these is the prevalence of hidden dependencies within code of apps. Based on this analysis, we propose a new notation called LondonTube aimed at making these hidden dependencies visible, thereby helping health scientists to create apps for themselves. A study showed that LondonTube reduced the time to create a cloud-mobile-web app by a factor of over 20, and it reduced questions about hidden dependencies.

Empirical studies have revealed that software developers spend 35%–50% of their time navigating through source code during development activities, yet fundamental questions remain: Are these percentages too high, or simply inherent in the nature of software development? Are there factors that somehow determine a lower bound on how effectively developers can navigate a given information space? Answering questions like these requires a theory that captures the core of developers' navigation decisions. Therefore, we use the central proposition of Information Foraging Theory to investigate developers' ability to predict the value and cost of their navigation decisions. Our results showed that over 50% of developers' navigation choices produced less value than they had predicted and nearly 40% cost more than they had predicted. We used those results to guide a literature analysis, to investigate the extent to which these challenges are met by current research efforts, revealing a new area of inquiry with a rich and crosscutting set of research challenges and open problems.

The design of programming tools is slow and costly. To ease this process, we developed a design pattern catalog aimed at providing guidance for tool designers. This catalog is grounded in Information Foraging Theory (IFT), which empirical studies have shown to be useful for understanding how developers look for information during development tasks. New design patterns, authored by members of the research community for the catalog, concretely explain how to apply IFT in tool design. In our evaluation, qualitative analyses revealed the community-written design patterns compared well in quality to patterns that we had ourselves published in a smaller, peer-reviewed catalog.