Abstract: Aspects of the disclosure relate to impact analysis in a next-release quality assurance environment for a software application. First log data associated with user navigation of user interface components in a production environment may be retrieved. A production navigational graph may be generated, where a node represents a user interface component visited by a user, and an edge representing a navigational link traversed by the user. Then, second log data associated with release notes for a next-release version of the software application maybe retrieved. Then, the computing platform may identify a change in a portion of a software code in the next-release version, and may identify, based on the production navigational graph, a user interface component and/or a link potentially impacted by the change. The production navigational graph may be provided, via an interactive graphical user interface, where the user interface component and/or the link is visually highlighted.

Abstract: Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.

Abstract: Aspects of the disclosure relate to impact analysis in a next-release quality assurance environment for a software application. First log data associated with user navigation of user interface components in a production environment may be retrieved. A production navigational graph may be generated, where a node represents a user interface component visited by a user, and an edge representing a navigational link traversed by the user. Then, second log data associated with release notes for a next-release version of the software application maybe retrieved. Then, the computing platform may identify a change in a portion of a software code in the next-release version, and may identify, based on the production navigational graph, a user interface component and/or a link potentially impacted by the change. The production navigational graph may be provided, via an interactive graphical user interface, where the user interface component and/or the link is visually highlighted.

Abstract: Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.

Abstract: Decoding a content of interest with optimal power usage. In an embodiment, a central processing unit (CPU) retrieves the frames of a data stream of interest from a secondary storage and stores them in a random access memory (RAM). The CPU forms an index table indicating the locations at which each of the frames is stored. The index table is provided to a decoder, which processes the frames in sequence to recover the original data from the encoded data. By using the index information, the power usage is reduced at least in an embodiment when the decoding is performed by an auxiliary processor.

Abstract: Decoding a content of interest with optimal power usage. In an embodiment, a central processing unit (CPU) retrieves the frames of a data stream of interest from a secondary storage and stores them in a random access memory (RAM). The CPU forms an index table indicating the locations at which each of the frames is stored. The index table is provided to a decoder, which processes the frames in sequence to recover the original data from the encoded data. By using the index information, the power usage is reduced at least in an embodiment when the decoding is performed by an auxiliary processor.