Microsoft Technology Licensing, LLC

Feng Liang

Cong Chen

Dongmei Zhang

Hang Li

Bin Zhao

Yingnong Dang

Shi Han

Song Ge

David Gregory Grant

Prashant Ratanchandani

Sk Kajal Arefin Imon

Navid Jalali Heravi

Ivan Michael Berg

Michael D Moshofsky

Chao Bian

Xiangpeng Zhao

Techniques for identifying a stack frame responsible for resource usage are described. For instance, techniques described herein enable a particular process and a particular stack frame and/or set of stack frames of the process that are high resource consumers to be identified. According to various implementations, resource usage of a process is observed and recorded over a period of time. A data structure is generated that characterizes the resource usage of the process over the sampling period. The data structure be evaluated to identify a stack frame that is responsible for excess resource consumption. In at least some implementations, a remedial procedure can be performed to attempt to reduce the process's resource usage.

Identifying a stack frame responsible for resource usage

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

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Issaquah, WA, United States of America

Feng Liang