Software component recommendation based on multiple trace runs

US10346292B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10346292-B2
Application numberUS-201415036338-A
CountryUS
Kind codeB2
Filing dateMar 27, 2014
Priority dateNov 13, 2013
Publication dateJul 9, 2019
Grant dateJul 9, 2019

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  1. Title

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  2. Abstract

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  5. First independent claim

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Abstract

Official abstract text for this publication.

Recommendations may be generated while calculating performance metrics from multiple uses of a software component. A tracing service may collect trace data from multiple uses of a software component, where each use may be done on different conditions. The performance metric analysis may identify various factors that may affect the performance of a software component, then present those factors to a user in different delivery mechanisms. In one such mechanism, a recommended set of hardware and software configurations may be generated as part of an operational analysis of a software component.

First claim

Opening claim text (preview).

What is claimed is: 1. A method performed on at least one computer processor, said method comprising: receiving a plurality of trace datasets, each of said trace datasets comprising a time series of performance data gathered while monitoring a first software component; analyzing said plurality of trace datasets to determine a differentiating factor that causes differences between said trace datasets, wherein determining the differentiating factor also includes identifying a set of one or more complementary components that, when executed, either increased or decreased an effectiveness of the first software component when the first software component was being executed; and presenting said differentiating factor or said set of one or more complementary components to a user. 2. The method of claim 1 , said differences comprising performance differences between said trace datasets. 3. The method of claim 2 , said differentiating factor comprising hardware differences. 4. The method of claim 3 , said differentiating factor further comprising software differences. 5. The method of claim 4 further comprising ranking a plurality of differentiating factors. 6. The method of claim 5 , said performance data comprising resource consumption data. 7. The method of claim 6 , said resource consumption data comprising at least one of a group composed of: processor resource consumption data; memory resource consumption data; and network resource consumption data. 8. The method of claim 6 , said performance data comprising usage data. 9. The method of claim 8 , said usage data comprising at least one of a group composed of: function call counts; and input parameters receives. 10. The method of claim 2 , said first software component being an application. 11. The method of claim 10 , a first trace dataset being gathered while executing said application on a first hardware configuration and a second trace dataset being gathered while executing said application on a second hardware configuration. 12. The method of claim 2 , said first software component being a reusable software component. 13. The method of claim 12 , a first trace dataset being gathered while executing said reusable software component as part of a first application, and a second trace dataset being gathered while executing said application as part of a second application. 14. A system comprising: a database comprising a plurality of trace datasets, each of said trace datasets being a time series of performance data gathered while monitoring a first software component; at least one processor; and an analysis engine operating on said at least one processor, said analysis engine that: receives a plurality of trace datasets, each of said trace datasets comprising a time series of performance data gathered while monitoring a first software component; and analyzes said plurality of trace datasets to determine a differentiating factor that causes differences between said trace datasets, wherein determining the differentiating factor also includes identifying a set of one or more complementary components that, when executed, either increased or decreased an effectiveness of the first software component when the first software component was being executed. 15. The system of claim 14 further comprising: an interface that receives a first request and returns said differentiating factor as a response to said first request. 16. The system of claim 15 , said interface being an application programming interface. 17. The system of claim 14 , said first software component being a reusable software component. 18. The system of claim 17 , a first trace dataset being collected while executing a first application using said reusable software component and a second trace dataset being collected while executing a second application using said reusable software component.

Assignees

Inventors

Classifications

  • Performance evaluation by tracing or monitoring · CPC title

  • Benchmarking · CPC title

  • Query execution · CPC title

  • for performance assessment · CPC title

  • for test execution, e.g. scheduling of test suites · CPC title

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Frequently asked questions

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What does patent US10346292B2 cover?
Recommendations may be generated while calculating performance metrics from multiple uses of a software component. A tracing service may collect trace data from multiple uses of a software component, where each use may be done on different conditions. The performance metric analysis may identify various factors that may affect the performance of a software component, then present those factors …
Who is the assignee on this patent?
Microsoft Technology Licensing Llc
What technology area does this patent fall under?
Primary CPC classification G06F11/3409. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Jul 09 2019 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 12 related publications on this page (citations in our corpus or others sharing the same primary CPC).