The concept of adaptation and self-adaptation of systems, in particular at runtime has caught the attention of the research community at large, both inside the domain of SPL as well as outside SPL in areas like models-at-runtime, self-adaptive systems, ubiquitous computing, etc. Dynamic software product lines (DSPL) constitute an emerging but promising research area where SPL concepts are entangled with runtime variability mechanisms, context information and runtime adaptation. However, the challenge to create better and smarter adaptable systems is still challenging. The workshop is intended as a highly interactive forum using a discussant format and to provide a forum for the exchange of ideas, to establish cooperations, and to bring industry and research together.
Organization of DSPL:
Development approaches and organizational models to set a DSPL
Comparison between SPL and DSPL models
New activities required by a DSPL
Upfront cost and post-deployment activities
Dynamic binding in SPL:
Multiple binding times and the transition between them
Rebinding issues after deployment
Binding times and system operational models
Dynamic library loading mechanisms
Dynamic variability management
Managing runtime variants and variation points
Runtime managers supporting variability
Optimization of runtime variability solutions
Variability constraints and dependencies managed at runtime
Relation between pre-runtime and runtime variabilities, their properties and models
DSPL and software engineering:
Runtime SPL requirements engineering
Runtime SPL architecture analysis
Dynamic component model for system of systems engineering
Trade-off analysis between adaptability and other qualities
Context-oriented features and runtime variability
Tools supporting any aspect related to DSPL and dynamic variability approaches
Collaborative features at runtime
DSPL application domains:
Role of dynamic variability in specific application domains (e.g., Automotive and smart vehicles, Critical systems, Drones, Cloud systems, Robotics)
Self-adaptive systems using dynamic variability techniques
Context variability for IoT software
Case studies
09月16日
2016
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