Abstract: Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.

Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.

Abstract: Techniques for obtaining a reaction of an individual to content and outputting the reaction of the individual with the content are described herein. The techniques may include obtaining the reaction of the individual as the content is displayed to the individual. The reaction may be obtained by capturing a video of the individual. The reaction of the individual and the content may be output at a same time to another individual. These techniques may also include obtaining the reaction of another individual as the content and the reaction of the individual are output to another individual.

Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.

Abstract: Automatically identifying insights from a dataset and presenting the insights graphically and in natural language text ranked by importance is provided. Different data types and structures in the dataset are automatic recognized and matched with a corresponding specific analysis type. The data is analyzed according to the determined corresponding analysis types, and insights from the analysis are automatically identified. The insights within a given insight type and between insight types are ranked and presented in order of importance in automatically generate charts that visually describe each insight and in natural language text that describes each insight in such a way that it may be understandable to a general audience who may not have a familiarity with statistics.

Abstract: Techniques for obtaining a reaction of an individual to content and outputting the reaction of the individual with the content are described herein. The techniques may include obtaining the reaction of the individual as the content is displayed to the individual. The reaction may be obtained by capturing a video of the individual. The reaction of the individual and the content may be output at a same time to another individual. These techniques may also include obtaining the reaction of another individual as the content and the reaction of the individual are output to another individual.

Abstract: Techniques for obtaining a reaction of an individual to content and outputting the reaction of the individual with the content are described herein. The techniques may include obtaining the reaction of the individual as the content is displayed to the individual. The reaction may be obtained by capturing a video of the individual. The reaction of the individual and the content may be output at a same time to another individual. These techniques may also include obtaining the reaction of another individual as the content and the reaction of the individual are output to another individual.

Abstract: The disclosure generally relates to code recommendation. In one embodiment, code snippets may be extracted using an invocation-centered code slicing process and then grouped according to the method usages. For each method usage, a representative code snippet may be selected and stored in the knowledge base in association with metadata. In operation, the programming context may be obtained and used to query the knowledge base to retrieve one or more code snippets for recommendation. In one embodiment, the recommended code snippets may be ranked to improve the utility and user friendliness, and the metadata may be used to provide variation points and possibly other auxiliary information to improve the operation efficiency and user experience.

Abstract: Techniques for error report processing are described herein. Error reports, received by a developer due to program crashes, may be organized into a plurality of “buckets.” The buckets may be based in part on a name and a version of the application associated with a crash. Additionally, a call stack of the computer on which the crash occurred may be associated with each error report. The error reports may be “re-bucketed” into meta-buckets to provide additional information to programmers working to resolve software errors. The re-bucketing may be based in part on measuring similarity of call stacks of a plurality of error reports. The similarity of two call stacks—a measure of likelihood that two error reports were caused by a same error—may be based in part on functions in common, a distance of those functions from the crash point, and an offset distance between the common functions.

Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.

Abstract: A system for frequent pattern mining uses two layers of processing: a plurality of computing nodes, and a plurality of processors within each computing node. Within each computing node, the data set against which the frequent pattern mining is to be performed is stored in shared memory, accessible concurrently by each of the processors. The search space is partitioned among the computing nodes, and sub-partitioned among the processors of each computing node. If a processor completes its sub-partition, it requests another sub-partition. The partitioning and sub-partitioning may be performed dynamically, and adjusted in real time.

Abstract: In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.

Abstract: The techniques described herein provide tools that summarize a dataset by creating a final set of segments that, when visually presented via a histogram or other data presentation tool, show the distribution of at least a portion of the data. To create the final set of segments, the techniques described herein may collect or receive a dataset with distinct values, and divide the dataset into a number of segments that is less than or equal to a segment presentation threshold (e.g., ten segments). After creating the final set of segments, the techniques may configure and/or present data visualizations, such as histograms, for the created segments so that an observer is provided with a good viewing experience.

Abstract: The disclosure generally relates to code recommendation. In one embodiment, code snippets may be extracted using an invocation-centered code slicing process and then grouped according to the method usages. For each method usage, a representative code snippet may be selected and stored in the knowledge base in association with metadata. In operation, the programming context may be obtained and used to query the knowledge base to retrieve one or more code snippets for recommendation. In one embodiment, the recommended code snippets may be ranked to improve the utility and user friendliness, and the metadata may be used to provide variation points and possibly other auxiliary information to improve the operation efficiency and user experience.

Abstract: A method for enabling graphical representation of aggregated data is provided. The method includes accessing aggregated data retrieved from a plurality of on-line sources and receiving selection of a portion of the data through a graphical user interface. The method further includes identifying attributes associated with the portion of the data and generating a graphical representation of the portion of the data.

Abstract: Techniques for detecting, analyzing, and/or reporting code clone are described herein. In one or more implementations, clone-code detection is performed on one or more source code bases to find true and near clones of a subject code snippet that a user (e.g., a software developer) expressly or implicitly selected. In one or more other implementations, code clone is analyzed to estimate the code-improvement-potential (such as bug-potential and code-refactoring-potential) properties of clones. One or more other implementations present the results of code clone analysis with indications (e.g., rankings) of the estimated properties of the respective the clones.

Abstract: Techniques for mining API method usage patterns from source code are described. These techniques include parsing the source code to generate API method call sequences that include an API method. These call sequences are clustered to obtain clusters. Based on the clusters, frequent closed sequences are determined and then clustered to obtain an API usage pattern. In addition, optimal clustering parameters may also be determined. In some instances, a graphical representation is generated based on the API usage pattern in response to a query associated with the API method.

Abstract: Techniques for error report processing are described herein. Error reports, received by a developer due to program crashes, may be organized into a plurality of “buckets.” The buckets may be based in part on a name and a version of the application associated with a crash. Additionally, a call stack of the computer on which the crash occurred may be associated with each error report. The error reports may be “re-bucketed” into meta-buckets to provide additional information to programmers working to resolve software errors. The re-bucketing may be based in part on measuring similarity of call stacks of a plurality of error reports. The similarity of two call stacks—a measure of likelihood that two error reports were caused by a same error—may be based in part on functions in common, a distance of those functions from the crash point, and an offset distance between the common functions.

Abstract: Techniques for error report processing are described herein. Error reports, received by a developer due to program crashes, may be organized into a plurality of “buckets.” The buckets may be based in part on a name and a version of the application associated with a crash. Additionally, a call stack of the computer on which the crash occurred may be associated with each error report. The error reports may be “re-bucketed” into meta-buckets to provide additional information to programmers working to resolve software errors. The re-bucketing may be based in part on measuring similarity of call stacks of a plurality of error reports. The similarity of two call stacks—a measure of likelihood that two error reports were caused by a same error—may be based in part on functions in common, a distance of those functions from the crash point, and an offset distance between the common functions.

Abstract: This disclosure describes a user interface and techniques for an interactive graphical representation of large scale data on a display. The disclosure describes how large scale data may be viewed using multiple linked charts. In one implementation, a user interface comprises an overview chart. The user may use chart controller(s) to designate one or more portions of the overview chart viewable in subsequent charts. The user may navigate between the overview chart and the subsequent charts using the chart controller(s).