Design patterns for monitoring adaptive ULS systems
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Cooperative network construction using digital germlines
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Avida-MDE: a digital evolution approach to generating models of adaptive software behavior
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Automatically Generating Behavioral Models of Adaptive Systems to Address Uncertainty
MoDELS '08 Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems
Using distributed w-learning for multi-policy optimization in decentralized autonomic systems
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Applying genetic algorithms to decision making in autonomic computing systems
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Automatically generating adaptive logic to balance non-functional tradeoffs during reconfiguration
Proceedings of the 7th international conference on Autonomic computing
Automatically discovering properties that specify the latent behavior of UML models
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part I
Survey: A survey on search-based software design
Computer Science Review
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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We describe an automated method to generating models of an autonomic system. Specifically, we generate UML state diagrams for a set of interacting objects, including the extension of existing state diagrams to support new behavior. The approach is based on digital evolution, a form of evolutionary computation that enables a designer to explore an enormous solution space for complex problems. In our application of this technology,an evolving population of digital organisms is subjected to natural selection,where organisms are rewarded for generating state diagrams that support key scenarios and satisfy critical properties as specified by the developer.To achieve this capability, we extended the Avida digital evolution platform to enable state diagram generation, and integrated Avida with third-party software engineering tools, e.g., the Spin model checker, to assess the generated state diagrams. To illustrate this approach, we successfully applied it to the generation of state diagrams describing the autonomous navigation behavior of a humanoid robot.