Software product lines: practices and patterns
Software product lines: practices and patterns
SEI's Software Product Line Tenets
IEEE Software
The Vision of Autonomic Computing
Computer
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
Computer
SPLC '06 Proceedings of the 10th International on Software Product Line Conference
System Architecture of an Autonomic Element
EASE '07 Proceedings of the Fourth IEEE International Workshop on Engineering of Autonomic and Autonomous Systems
On the Design and Development of Program Families
IEEE Transactions on Software Engineering
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
Dynamic Software Product Lines
Computer
Applying Software Product Lines to Build Autonomic Pervasive Systems
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
Engineering Self-Adaptive Systems through Feedback Loops
Software Engineering for Self-Adaptive Systems
SC'08 Proceedings of the 7th international conference on Software composition
On interacting control loops in self-adaptive systems
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Knowledge evolution in autonomic software product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
Towards autonomic software product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
Using domain features to handle feature interactions
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Optimized composition of performance-aware parallel components
Concurrency and Computation: Practice & Experience
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We describe ongoing work on a variability mechanism for Autonomic Software Product Lines (ASPL). The autonomic software product lines have self-management characteristics that make product line instances more resilient to context changes and some aspects of product line evolution. Instances sense the context, selects and bind the best component variants to variation-points at run-time. The variability mechanism we describe is composed of a profile guided dispatch based on off-line and on-line training processes. Together they form a simple, yet powerful variability mechanism that continuously learns, which variants to bind given the current context and system goals.