Abstract: In one embodiment, dynamic user private networks are virtually segmented within a shared virtual network. A network control system maintains the dynamic logical segmentation of the shared virtual network. User entities (e.g., user devices and/or services) are communicatively coupled to respective personal virtual networks via endpoints of access devices. Each of these endpoints is associated with a corresponding user private network. Responsive in real-time to automated processing of a received electronic particular user request, the network control system automatically modifies the dynamic logical segmentation of the shared virtual network to move a particular user entity on the shared virtual network to newly being on the first dynamic user private network without being disconnected from the shared virtual network. One embodiment uses different user private network identifiers (UPN-IDs) associated with endpoints and received packets to identify their respective user private network.

Abstract: A probabilistic machine learning model is generated to identify potential bugs in a source code file. Source code files with and without bugs are analyzed to find features indicative of a pattern of the context of a software bug, wherein the context is based on a syntactic structure of the source code. The features may be extracted from a line of source code, a method, a class and/or any combination thereof. The features are then converted into a binary representation of feature vectors that train a machine learning model to predict the likelihood of a software bug in a source code file.