Microsoft Technology Licensing, LLC

Song Ge

Dongmei Zhang

Yingnong Dang

Shi Han

Hang Li

Bin Zhao

He Huang

Bin Zhu

Haidong Zhang

Cong Chen

Prashant Ratanchandani

Yingjun Qiu

Feng Liang

Chao Bian

Xiangpeng Zhao

Yuanyuan Tang

According to implementations of the subject matter described herein, there is provided a solution for data embedding and data extraction in images. To perform data embedding, a target region for data embedding is determined from a source image, such as a chart image. Target data to be embedded is converted into a sequence of logical values represented in a predetermined format. Based on the sequence of logical values, image values of one or more image elements in the target region are changed such that the changed image values in the target region can be used to convey the sequence of logical values corresponding to the target data. The variations in image values are within a predetermined range such that no significant data distortion of perception distortion is caused by the data embedding. In a subsequent process, the embedded data can also be easily extracted from the image for use.

Data embedding and data extraction in image

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.

Frequent pattern mining

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&#x2014;a measure of likelihood that two error reports were caused by a same error&#x2014;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.

Error report processing using call stack similarity

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.

Code-clone detection and analysis

Execution traces are collected from multiple execution instances that exhibit performance issues such as slow execution. Call stacks are extracted from the execution traces, and the call stacks are mined to identify frequently occurring function call patterns. The call patterns are then clustered, and used to identify groups of execution instances whose performance issues may be caused by common problematic program execution patterns.

Analyzing software performance issues

Growing community of inventors

Beijing, China

Song Ge