The LondonTube environment includes a visual programming language to ease creation of apps distributed at runtime over mobile devices, browsers and the cloud. However, a typical programmer still learning the language would struggle with debugging a program of realistic size, in large part due to the difficulty of finding and understanding bugs. We have implemented an IDE plugin aimed at showing where in the code the computation breaks down and helping the programmer to understand why that code might not be working. In a between-subjects experiment, novice LondonTube users with these new features asked fewer questions about the language than those without, and they gave the enhanced environment higher usability ratings.

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.

Summary form only given. Above showsthat the next generation of mobile applications for MOPs need to be part of a much large mobile information system. Such system should be capable of receiving information from a large number of users, intelligently aggregating the information to useful, meaningful, real-time information and making them available to users. Users should be able to make queries and quickly find information that would enhance their day to day activities. It should also have the functionalities to detect different and unusual situations, provide alerts to users in unusual circumstances and emergencies so that users can respond accordingly. These next generation mobile applications for MOPs will open up a very large market currently estimated to be around 3 billion people [13]. These applications will empower them to be information users as well as information producers. Such applications would result in up lifting of economic conditions of the MOPs and transformation of social processes.