Abstract: A method of providing an error occurrence estimate for a proposed software update, before the proposed software update is created, includes training a learning process to cause the learning process to learn a correlation between the complexity of the previous software updates and the error occurrences of the previous software updates. The complexity information may include the number of lines of code and the number of check-in operations that occurred in connection with creation of the previous software updates. The trained learning process is then provided with expected complexity information of a proposed software update, and used to generate an error estimate including the number of errors that are likely to occur, the severity of the errors that are likely to occur, and the amount of software developer time that should be expected to be incurred to correct the errors.

Abstract: A data processing device includes persistent storage and a software development manager. The persistent storage stores a code repository. The software development manager generates commit impact information based on: pre-commit code for a code base stored in the code repository, and dependency data based on committed code of the code base; displays a pre-commit analysis graphic user interface (GUI) reflecting the commit impact information and testing information for the pre-commit code; obtains, using the displayed pre-commit analysis GUI, reviewer feedback regarding approval of the pre-commit code; makes a determination, based on the reviewer feedback, that the pre-commit code has been approved; in response to the determination: performs limited testing of the code base based upon the dependency data to obtain testing results; and commits the pre-commit code to the committed code when the testing results match a predetermined behavior.

Abstract: A method of providing an error occurrence estimate for a proposed software update, before the proposed software update is created, includes training a learning process to cause the learning process to learn a correlation between the complexity of the previous software updates and the error occurrences of the previous software updates. The complexity information may include the number of lines of code and the number of check-in operations that occurred in connection with creation of the previous software updates. The trained learning process is then provided with expected complexity information of a proposed software update, and used to generate an error estimate including the number of errors that are likely to occur, the severity of the errors that are likely to occur, and the amount of software developer time that should be expected to be incurred to correct the errors.

Abstract: A data processing device includes persistent storage and a software development manager. The persistent storage stores a code repository. The software development manager generates commit impact information based on: pre-commit code for a code base stored in the code repository, and dependency data based on committed code of the code base; displays a pre-commit analysis graphic user interface (GUI) reflecting the commit impact information and testing information for the pre-commit code; obtains, using the displayed pre-commit analysis GUI, reviewer feedback regarding approval of the pre-commit code; makes a determination, based on the reviewer feedback, that the pre-commit code has been approved; in response to the determination: performs limited testing of the code base based upon the dependency data to obtain testing results; and commits the pre-commit code to the committed code when the testing results match a predetermined behavior.

Abstract: A native data collection component may be implemented as a tool, appliance, application and/or utility, and including physical and/or logical implementations thereof. The native data collection component may be responsible for data orchestration, data collection, and aggregation and distribution of collected data from among multiple sources. Accordingly, the native data collection component may be independent of a particular data source and act as an independent performance characteristic collection mechanism. In an embodiment, the native data collection component may include three devices/sub-components: a data orchestration device, a data collection interface and a data aggregation and distribution device. Each sub-component may be service based and communication with each service may be done using state-less protocols.

Abstract: A native data collection component may be implemented as a tool, appliance, application and/or utility, and including physical and/or logical implementations thereof. The native data collection component may be responsible for data orchestration, data collection, and aggregation and distribution of collected data from among multiple sources of a storage area network (SAN). The native data collection component is not required to be installed as an agent on any data source of the SAN, and the native data collection component may be independent of a particular data source and act as an independent performance characteristic collection mechanism. In an embodiment, the native data collection component may include three devices/sub-components: a data orchestration device, a data collection interface and a data aggregation and distribution device. Each sub-component may be service based and communication with each service may be done using state-less protocols.

Abstract: A system for controlled-tuning path performance data collection through a single application control. In an embodiment, performance data collection may be turned on or off from an application host running a path performance data application via a user interface. The tool may automatically update performance data collection characteristics as the application host I/O data path changes according to user controlled settings but without requiring further user intervention during the updating. In various embodiments, the tool may update continuously and/or at specified intervals. Turning on path performance data collection on the application host via the user interface may automatically set up synchronized performance data collection for all managed objects within I/O data path(s) of the managed objects.