Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Abstract: A computer implemented method includes receiving an exception generated based on programming code, generating exception features from the received exception, the generated exception features being generated based on a set exception features derived from search logs, and executing a machine learning model on the received exception and generated exception features to provide information from the search logs identified as most helpful to resolve the received exception, wherein the machine learning model was trained on training data comprising extracted exceptions and the set of exception features derived from the search logs.

Abstract: Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics.

Abstract: Embodiments promote searcher productivity and efficient search engine usage by using extraquery context to detect a searcher's intent, and using detected intent to match searches to well-suited search providers. Extraquery context may include cursor location, open files, and other editing information, tool state, tool configuration or environment, project metadata, and other information external to actual search query text. Search intent may be code (seeking snippets) or non-code (seeking documentation), and sub-intents may be distinguished for different kinds of documentation or different programming languages. Search provider capabilities may reflect input formats such as natural language or logical operator usage, or content scope such as web-wide or local, or other search provider technical characteristics.

Abstract: Systems and methods for automatic recognition of entities related to cloud incidents are described. A method, implemented by at least one processor, for processing cloud incidents related information, including entity names and entity values associated with incidents having a potential to adversely impact products or services offered by a cloud service provider is provided. The method may include using at least one processor, processing the cloud incidents related information to convert at least words and symbols corresponding to a cloud incident into machine learning formatted data. The method may further include using a machine learning pipeline, processing at least a subset of the machine learning formatted data to recognize entity names and entity values associated with the cloud incident.

Abstract: A computer implemented method includes receiving an exception generated based on programming code, generating exception features from the received exception, the generated exception features being generated based on a set exception features derived from search logs, and executing a machine learning model on the received exception and generated exception features to provide information from the search logs identified as most helpful to resolve the received exception, wherein the machine learning model was trained on training data comprising extracted exceptions and the set of exception features derived from the search logs.

Abstract: A method for generating productivity insights includes receiving health data for a user of a productivity evaluation service. From the health data, health behaviors and health effects of the user are determined. Productivity data for the user is received, and from the productivity data, productivity behaviors and productivity effects of the user are determined. Associations between changes in the health data and changes in the productivity data are identified. Based on one of the associations, a productivity insight is generated for the user including a prompt to engage in a health behavior that is associated with a desirable productivity effect.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: A system is described herein that predicts defects in a portion of code of an application that is configured to execute on a computing device. Versions of code are analyzed to locate change bursts, which are alterations to at least one portion of code over time-related events. If a change burst is identified, defects are predicted with respect to the code based at least in part upon the identified change burst.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: A system is described herein that predicts defects in a portion of code of an application that is configured to execute on a computing device. Versions of code are analyzed to locate change bursts, which are alterations to at least one portion of code over time-related events. If a change burst is identified, defects are predicted with respect to the code based at least in part upon the identified change burst.