Modern software is so complicated that it is often infeasible to get a good understanding of a system\u2019s dynamic behaviour simply from its source code. Commodity operating systems are a good example: they comprise numerous separately-authored components, large numbers of interacting threads, and extensibility mechanisms that allow new components to be plugged in based on boot-time or run-time configuration settings. Ideally it should be possible to understand this kind of complex system by capturing dynamic traces of its behaviour and then applying machine learning techniques to these traces to elucidate the structure present in them. In practice, this is extremely difficult because such systems are usually poorly specified and structured, and component interactions are largely unconstrained.<\/p>\n","protected":false},"excerpt":{"rendered":"
Modern software is so complicated that it is often infeasible to get a good understanding of a system\u2019s dynamic behaviour simply from its source code. Commodity operating systems are a good example: they comprise numerous separately-authored components, large numbers of interacting threads, and extensibility mechanisms that allow new components to be plugged in based on 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