Learning Communication Patterns in Singularity

Modern software is so complicated that it is often infeasible to get a good understanding of a sys-tem's dynamic behaviour simply from its source code. Commodity operating systems are a good example: they comprise numerous separately-authored components, large numbers of interact-ing threads, and extensibility mechanisms that al-low new components to be plugged in based on boot-time or run-time configuration settings. Ide-ally 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 struc-ture present in them. In practice, this is extremely di cult because such systems are usually poorly specified and structured, and component interac-tions are largely unconstrained [2].