Abstract: Described is a technology by which software instrumentation data collected from user program sessions are analyzed to output an analysis report or the like via example methods and an architecture configured for efficient operation. A client component queries a service for analysis related information. To process the query, the service works with a data manager, and via a high dimensional analysis component may use information processed from the software instrumentation data, such as in the form of one or more inverted indexes and/or raw value files. The service may include a usage analysis component, a feature recognition component that locates features from command sequences, a user recognition component and/or a program reliability component. One or more counterpart components at the client may generate analysis reports or the like based on the query results. The client also may maintain user libraries and feature libraries to facilitate analyses.

Abstract: Described is a technology by which high dimensional data may be efficiently analyzed, including by filtering, grouping, aggregating and/or sorting operations to provide an analysis result. For efficiency in the analysis, an inverted index may be built (e.g., as part of filtering), and/or a hash structure (e.g., as part of grouping). Analysis parameters specify dimensions, on which union and/or intersection operations are performed to provide a final dataset. The analysis tool provides a user interface for inputting analysis parameters and outputting information corresponding to an analysis result. The analysis tool may sort the information corresponding to the analysis result, e.g., to output the topmost or bottommost results.

Abstract: Described is a technology by which software instrumentation data collected during software program usage sessions is analyzed to identify potential problems with software program usage, such as based on frequency of problem occurrence during the usage sessions. Reliability metrics may be calculated from the information. Failure data additionally collected during the usage sessions may be accessed to derive details that correspond to the potential problems. In one example, the information may be analyzed to determine which alerts and/or asserts occurred most often, and/or to determine a relationship between user interface control operations (e.g., clicks and usage of commands) and alerts or asserts.

Abstract: Described is a technology for analyzing usage of a software program's features. Software instrumentation data is during actual user program usage sessions. The collected data is then processed to determine various feature usage counts and other information, cross-feature usage (e.g., among users who use a feature, how many use another feature or program), and characteristics of feature users, e.g., how long, how much, how often and how extensive feature users use a program. Session analysis may be performed to provide information about the number of sessions in which a set of features occur. Feature usage trends over time may also be determined via analysis. A user interface is described for facilitating selection of one or more features to analyze, for facilitating selection of a group of users, and/or for outputting results corresponding to the analysis.

Abstract: Described is a technology by which software instrumentation data collected during software program usage sessions is analyzed to identify potential problems with software program usage, such as based on frequency of problem occurrence during the usage sessions. Reliability metrics may be calculated from the information. Failure data additionally collected during the usage sessions may be accessed to derive details that correspond to the potential problems. In one example, the information may be analyzed to determine which alerts and/or asserts occurred most often, and/or to determine a relationship between user interface control operations (e.g., clicks and usage of commands) and alerts or asserts.

Abstract: Described is a technology by which software instrumentation data collected from user program sessions are analyzed to output an analysis report or the like via example methods and an architecture configured for efficient operation. A client component queries a service for analysis related information. To process the query, the service works with a data manager, and via a high dimensional analysis component may use information processed from the software instrumentation data, such as in the form of one or more inverted indexes and/or raw value files. The service may include a usage analysis component, a feature recognition component that locates features from command sequences, a user recognition component and/or a program reliability component. One or more counterpart components at the client may generate analysis reports or the like based on the query results. The client also may maintain user libraries and feature libraries to facilitate analyses.

Abstract: Described is a technology by which high dimensional data may be efficiently analyzed, including by filtering, grouping, aggregating and/or sorting operations to provide an analysis result. For efficiency in the analysis, an inverted index may be built (e.g., as part of filtering), and/or a hash structure (e.g., as part of grouping). Analysis parameters specify dimensions, on which union and/or intersection operations are performed to provide a final dataset. The analysis tool provides a user interface for inputting analysis parameters and outputting information corresponding to an analysis result. The analysis tool may sort the information corresponding to the analysis result, e.g., to output the topmost or bottommost results.