Microsoft Technology Licensing, LLC

Suman Kumar Nath

Jie Jerry Liu

Douglas Christopher Burger

Oriana Riva

Feng Zhao

Aman Kansal

Prashanth Hosabettu Mohan

Lenin Ravindranath Sivalingam

Michel Goraczko

Earlence Tezroyd Fernandes

Jitendra Dattatraya Padhye

Nissanka Arachchige Bodhi Priyantha

Badrish Chandramouli

Dimitrios Lymberopoulos

Leonidas Rigas

Sorabh Kumar Gandhi

Lin Xiao

Wenbo He

Fazle Elahi Faisal

Wenchao Zhou

Galen Andrew Reeves

Yanif Ahmad

Gong Chen

Peter C Shrosbree

Tanakorn Leesatapornwongsa

Di Wang

Anirudh Badam

Jeremy Eric Elson

Lei Zhang

Badriddine Khessib

Changhu Wang

Gerald Reuben DeJean

Tadayoshi Kohno

Sean Anderson Bowles

Laura Marie Caulfield

Chetan Bansal

Yuxiao Hu

Franziska Roesner

Charles Loboz

Mohammed Shoaib

Nissanka Bodhi Priyantha

Jian Huang

Dong Wei

Bikash Sharma

Haitao Wang

Somak Chattopadhyay

Madan Musuvathi

Sergey Kanzhelev

Marcel Gavriliu

Azalia Mirhoseini

Seung Ho Lee

Manish Shetty Molahalli

Nitesh Mor

Temitope Oluwafemi

Xiaozhu Lin

Arsalan Tavakoli-Shiraji

Slawomir Smyl

Oliver Andrzej Kennedy

Pavan Kumar Athivarapu

Yongjian Hu

Vibhor Rastogi

Andre Santanche

Kamil Ozgur Arman

Md Tanzirul Azim

Siamak Ahari

Systems and methods are provided for automatically generating a fault-enabled software development kit (SDK) to test an application. The generating includes determining one or more faults based on codes associated with the SDK and a frequency of occurrences of faults while executing the SDK. A fault injector automatically injects the determined faults in select layers of code in the SDK and generates fault configuration data associated with the automatically injected faults in the SDK. The fault configuration data describes faults that have been injected in the fault-enabled SDK to test an application. The fault-enabled SDK tests the application without needs for modifying the application code for testing purposes. The fault-enabled SDK further provides types of faults that may be common in using the SDK without requiring application developers to have detailed understanding of internals of the SDK to test the application with simulated faults.

Generating fault conditions using a fault-enabled software development kit

Machine-learned prediction of a blame frame of a crash stack. Specifically, a crash stack associated with a crash is parsed into a sequence of frames. The blame frame of the crash stack is estimated by, for each of a plurality of the sequence of frames, identifying a plurality of features of the corresponding frame, feeding the plurality of features to a neural network, and using the output of the neural network to make a prediction on whether the corresponding frame is a blame frame of the crash. If this is done during training time, the predicted blame frame can be compared against the actual blame frame, resulting in an adjustment of the neural network. Through appropriate featurization of the frames, and by use of the neural network, the prediction can be made cross-application and considering the context of the frame within the crash stack.

Crash localization using crash frame sequence labelling

Systems and methods for extracting and sharing application-related user data are disclosed. A method may include extracting in-app data for at least one of the plurality of apps running on a computing device, the in-app data including content consumed by a user while the at least one app is running, and/or at least one user action taken in connection with the content. Using an entity template associated with the app, a plurality of text strings within the in-app data are classified into at least one of a plurality of data types specified by the template. At least one user data item (UDI) may be generated by combining at least a portion of the classified plurality of text strings, the at least one UDI being accessible by a second app, an operating system running on the, a service of the operating system, and/or a service running on at least another device.

System and method for extracting and sharing application-related user data

Computer systems and methods for generating and interacting with a micro-service framework are provided. A micro-service corresponds to one or more deep link/API calls that carry out some particular function. A static analysis of an app is conducted, from one or more starting sources of the app to identify one or more valid and feasible execution paths, as well as corresponding input parameters within the app. Each valid execution path with corresponding input parameters represent a 'deep link' or 'API' for that app. The information regarding the deep link is collected and stored as a micro-service in a micro-service catalog. A micro-service framework is implemented that receives a micro-service request (i.e., a request that the micro-service be carried out on behalf of a computer user) from a UX client and executes that micro-service request via execution of the deep link.

Micro-service framework derived from third-party apps

Various technologies described herein pertain to prefetching content units. A prefetch request is transmitted to a server from a client device. The prefetch request includes data indicative of probabilities of slots for content units being available during an upcoming time period. The probabilities can be based on likely interaction with application(s) executed by the client device during the upcoming time period. Prefetched content units assigned to the client device for the upcoming time period can be received from the server responsive to the prefetch request. One or more of the prefetched content units can be served for display on a display screen of the client device during execution the application(s). Further, statuses of the prefetched content units can be monitored, and information that specifies a subset of the prefetched content units that are unlikely to be displayed on the display screen prior to corresponding deadlines for expiration can be transmitted.

Energy-efficient content serving

System and methods are provided for optimal error detection in programmatic environments through the utilization of at least one user-defined condition. Illustratively, the conditions can include one or more triggers initiating the collection of log data for methods associated with the provided at least one condition. Operatively, the disclosed systems and methods observe the run-time of the programmatic environment and initiate the collection of log data based on the occurrence of a condition trigger. A rank score can also be calculated to rank the methods associated with the defined condition to isolate those methods that have higher probability of causing the defined condition. Dynamic instrumentation of the methods associated with the user defined conditions during run time are used to calculate the rank score, which is used for ranking the methods.

System and methods for optimal error detection in programmatic environments

Various technologies described herein pertain to allocating content units to client devices. Content units can be requested from an exchange by a server. The content units can be received by the server from the exchange, where the content units each has a corresponding deadline for expiration. Further, an indication of a penalty can be transmitted to the exchange if one of the content units is not displayed by one or more client devices upon expiration of the corresponding deadline.

Techniques are described for validating stateful app links. Validation can be performed when stateful app links are created, activated, shared, or at other times. Validation can be performed to determine whether a stateful app link has a dependency on a resource external to the mobile application. Validation can also be performed to detect other issues, such as security issues, privacy issues, or other issues.

Validating stateful dynamic links in mobile applications

The privacy-preserving cookie generator implementations described herein create a privacy-preserving data structure (also called a privacy-preserving cookie herein) that is used to provide personalization for online services without user tracking. In some implementations the privacy-preserving cookie generator encodes a user profile (for example, based on a user's online activity) into a data structure that has naturally occurring noise and that efficiently supports noise addition. In one implementation a Bloom filter is used to create the encoded profile. Additional noise is injected into the encoded profile to create an obfuscated user profile in the form of a privacy-preserving data structure. The privacy-preserving data structure or cookie can be attached to an online service request and sent over a network to an online service provider which can use it fulfill the services request, providing a somewhat personalized result while the user's privacy is maintained.

Privacy-preserving cookies for personalization without user tracking

The deep application crawling technique described herein crawls one or more applications, commonly referred to as 'apps', in order to extract information inside of them. This can involve crawling and extracting static data that are embedded within apps or resource files that are associated with the apps. The technique can also crawl and extract dynamic data that apps download from the Internet or display to the user on demand, in order to extract data. This extracted static and/or data can then be used by another application or an engine to perform various functions. For example, the technique can use the extracted data to provide search results in response to a user query entered into a search engine. Alternately, the extracted static and/or dynamic data can be used by an advertisement engine to select application-specific advertisements. Or the data can be used by a recommendation engine to make recommendations for goods/services.

Deep application crawling

Examples of the disclosure enable a plurality of applications to be managed. Some examples include a mobile device that receives a template associated with an application, captures one or more user interactions with the application, and determines one or more user interests by comparing the one or more user interactions with a plurality of entities included in the template. Examples of the disclosure enable the mobile device to analyze the user interactions to provide valuable information about user interest, preference, and/or intent and provide tailored services to a user.

De-siloing applications for personalization and task completion services

Operations of a variety of components of a storage system stack are redefined to make the system more efficient when the underlying media has a 'multi-log' type interface such as the case with NAND flash SSD memory or shingled magnetic recording media. The responsibilities of components of the storage system stack are modified such that each responsibility is performed at the most efficient component (level of abstraction) of the storage stack.

Data storage system with data storage devices operative to manage storage device functions specific to a particular data storage device

Growing community of inventors

Redmond, WA, United States of America

Suman Kumar Nath