ISAR: Ein Autorensystem für Interaktive Tische

Developing augmented reality systems involves several challenges, that prevent end users and experts from non-technical domains, such as education, to experiment with this technology. In this research we introduce ISAR, an authoring system for augmented reality tabletops targeting users from non-technical domains. ISAR allows non-technical users to create their own interactive tabletop applications and experiment with the use of this technology in domains such as educations, industrial training, and medical rehabilitation.Die Entwicklung von Augmented-Reality-Systemen ist mit mehreren Herausforderungen verbunden, die Endbenutzer und Experten aus nicht-technischen Bereichen, wie z.B. dem Bildungswesen, daran hindern, mit dieser Technologie zu experimentieren. In dieser Forschung stellen wir ISAR vor, ein Autorensystem für Augmented-Reality-Tabletops, das sich an Benutzer aus nicht-technischen Bereichen richtet. ISAR ermöglicht es nicht-technischen Anwendern, ihre eigenen interaktiven Tabletop-Anwendungen zu erstellen und mit dem Einsatz dieser Technologie in Bereichen wie Bildung, industrieller Ausbildung und medizinischer Rehabilitation zu experimentieren

Towards Automation of Iteration Planning

Iterations are time-boxed periods with an intended outcome that is often a set of implemented requirements. Iterations are part of most common software development lifecycle models. Planning of iterations is a non-trivial task due to the multi-dimensional criteria. (1) The first dimension concerns the question what shall be completed in the iteration, also referred to as “release planning”. Decisions in this dimension are based on criteria such as dependencies and priorities of requirements. (2) The second dimension concerns the decision, which project participant should work on which task, also referred to as “task assignment”. Decisions in this dimension are based on criteria such as the expertise and the workload of the developers. The decisions in both dimensions are considerably complex. Therefore several approaches exist to semi-automatically support the decisions limited to one of the two dimensions mentioned above. None of the existing approaches considers both dimensions at the same time. In this paper we propose a combination of approaches from semi-automatic release planning and from semi-automatic task assignment. This results in a semi-automated two-dimensional solution for the problem of iteration planning, We suggest the use of a genetic algorithm to optimize the resulting iteration plans in both dimensions of the problem