Integration of Reusable Systems

Software reuse and integration has been described as the process of creating software systems from existing software rather than building software systems from scratch. Whereas reuse solely deals with the artifacts creation, integration focuses on how reusable artifacts interact with the already existing parts of the specified transformation. Currently, most reuse research focuses on creating and integrating adaptable components at development or at compile time. However, with the emergence of ubiquitous computing, reuse technologies that can support adaptation and reconfiguration of architectures and components at runtime are in demand.

This edited book includes 15 high quality research papers written by experts in information reuse and integration to cover the most recent advances in the field. These papers are extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration and IEEE International Workshop on Formal Methods Integration, which was held in San Francisco in August 2013.

Latest research on Formal Methods Integration Best papers of the IEEE International Workshop on Formal Methods Integration - IEEE FMi 2013, held at San Francisco, USA, August 14-16 2013 Written by experts in the field Includes supplementary material: sn.pub/extras

Inhalt
Cloud-Based Tasking, Collection, Processing, Exploitation, and Dissemination in a Case-Based Reasoning System.- Simulation-Based Validation for Smart Grid Environments: Framework and Experimental Results.- An Institution for Alloy and its Translation to Second-Order Logic.- A Framework for Verification of SystemC Designs Using SystemC Waiting State Automata Waiting State Automata Waiting State Automata.- Formal MDE-Based Tool Development.- Formal Modeling and Analysis of Learning-Based Routing in Mobile Wireless Sensor Networks Mobile Wireless Sensor Networks Mobile Wireless Sensor Networks On the Use of Anaphora Resolution for Workflow Extraction.- A Minimum Description Length Technique for Semi Supervised Time Series Classification Time Series Classification Time Series Classification.- Interpreting Random Forest Classification Models Using a Feature Contribution Method.