Abstract: A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.

Abstract: A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.

Abstract: A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.

Abstract: Methods, systems, and computer program products for automatically suggesting a temporal opportunity for writing one or more sequel articles via artificial intelligence are provided herein. A computer-implemented method includes extracting one or more types of information from a prior written document; automatically determining, based on the extracted information, at least one temporal opportunity for generating a follow-up written document to the prior written document; automatically generating a follow-up written document to the prior written document, the follow-up written document being written in a style that indicates that it is in response to the prior written document, in accordance with the at least one determined temporal opportunity, and based on (i) one or more items of information, related to the extracted information, derived from one or more web sources, and (ii) a writing model attributed to a user.

Abstract: The present disclosure relates to a method performed by a network node (54) in a communication network (50) comprising a plurality of communication devices (51), for finding temporally connected connection patterns of the communication devices in the network. The method comprises identifying signalling between the communication devices during a predefined time duration to form a main communication graph in which the plurality of communication devices are nodes. The method also comprises partitioning the main communication graph for a time period comprised in said time duration to capture temporally connected signalling between some of the communication devices as illustrated in the main communication graph in a partitioned communication graph. The method also comprises forming at least one subgraph from the partitioned communication graph, comprising m nodes corresponding to m communication devices of said some of the communication devices.

Abstract: Methods, systems, and computer program products for automatically suggesting a temporal opportunity for writing one or more sequel articles via artificial intelligence are provided herein. A computer-implemented method includes extracting one or more types of information from a prior written document; automatically determining, based on the extracted information, at least one temporal opportunity for generating a follow-up written document to the prior written document; automatically generating a follow-up written document to the prior written document, the follow-up written document being written in a style that indicates that it is in response to the prior written document, in accordance with the at least one determined temporal opportunity, and based on (i) one or more items of information, related to the extracted information, derived from one or more web sources, and (ii) a writing model attributed to a user.

Abstract: A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.

Abstract: The present disclosure relates to a method performed by a network node (54) in a communication network (50) comprising a plurality of communication devices (51), for finding temporally connected connection patterns of the communication devices in the network. The method comprises Identifying signalling identifying signalling between the communication devices during a predefined time duration to form a main communication graph in which the plurality of communication devices are nodes. The method also comprises partitioning the main communication graph for a time period comprised in said time duration to capture temporally connected signalling between some of the communication devices as illustrated in the main communication graph in a partitioned communication graph. The method also comprises forming at least one subgraph from the partitioned communication graph, comprising m nodes corresponding to m communication devices of said some of the communication devices.