We Don't Need Another Hero? The Impact of "Heroes" on Software Development

A software project has "Hero Developers" when 80% of contributions are delivered by 20% of the developers. Are such heroes a good idea? Are too many heroes bad for software quality? Is it better to have more/less heroes for different kinds of projects? To answer these questions, we studied 661 open source projects from Public open source software (OSS) Github and 171 projects from an Enterprise Github. We find that hero projects are very common. In fact, as projects grow in size, nearly all project become hero projects. These findings motivated us to look more closely at the effects of heroes on software development. Analysis shows that the frequency to close issues and bugs are not significantly affected by the presence of project type (Public or Enterprise). Similarly, the time needed to resolve an issue/bug/enhancement is not affected by heroes or project type. This is a surprising result since, before looking at the data, we expected that increasing heroes on a project will slow down howfast that project reacts to change. However, we do find a statistically significant association between heroes, project types, and enhancement resolution rates. Heroes do not affect enhancement resolution rates in Public projects. However, in Enterprise projects, the more heroes increase the rate at which project complete enhancements. In summary, our empirical results call for a revision of a long-held truism in software engineering. Software heroes are far more common and valuable than suggested by the literature, particularly for medium to large Enterprise developments. Organizations should reflect on better ways to find and retain more of these heroesComment: 8 pages + 1 references, Accepted to International conference on Software Engineering - Software Engineering in Practice, 201

Open Educational Resources

<p>Presentation by Normal Bier of the Open Learning Initiative (OLI) on open education resources.</p

Unleashing the Doer Effect: Adding Interactivity to Open Content

Learning science research is clear about the Doer Effect: students learn best by doing interacting with content through questions, activities, and simulations. Learn about various options for applying the Doer Effect to OER content, and collaborate with others to create learning activities that put these methods into practice

Aspects of Ambient UX Design Within Design-to-Robotic-Production and -Operation Processes

Ambient User Experience design in architecture implies consideration of various intersecting and sometimes overlapping design fields such as interaction and architectural design with the aim to achieve a continuous and cohesive user experience across devices, time, and space. In this paper, Ambient User Experience design is explored in relation to Design-to-Robotic-Production and -Operation processes developed at TU Delft, which link computational design with robotic production and operation. Several case studies involving the integration of sensor-actuators into the built environment are discussed with respect to mapping activities through constraints and enablers and designing with the time as a variable.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Architectural Engineerin

Experiments as a service Infrastructure (EASI).

This project introduces the Experiments As a Service Infrastructure (EASI), which lowers the barriers to conducting randomized experiments that compare alternative ways of designing digital learning experiences, as well as analyzing the data derived from the systems to rapidly change what future people receive. The project brings together multidisciplinary researchers around the shared problem of testing ideas for improving and personalizing educational resources. The research also advances (1) the science of learning and instruction; (2) methods for analyzing complex educational data, and (3) machine learning algorithms that use data to improve educational experiences. Improving learning and teaching increases people's knowledge and gives them the ability to solve problems they care about, driving their personal and career success and increasing society's human capital.</p