DYNAMIC RANKING OF PERFORMANCE ISSUES FOR APPLICATIONS

• US 20160378648A1
• Filed: 06/29/2015
• Published: 12/29/2016
• Est. Priority Date: 06/29/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Mangala Basavanna, Sachidanand, Ekambaram, Vijay, Mathur, Ashish K., Rajput, Nitendra, Sharma, Vivek
• Granted Patent: US 10,241,891 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/00 Error detection; Error correction; Monitoring error detection, correction or monitoring in information storage based on relative movement between record carrier and transducer G11B20/18; monitoring, i.e. supervising the progress of recording or reproducing G11B27/36; in static stores G11C29/00
• CPC Class Codes: G06F 11/301 where the computing system is a virtual computing platform, e.g. logically partitioned systems virtual machines G06F9/45533; logical partitioning of resources G06F9/5077
• CPC Class Codes: G06F 11/302 where the computing system component is a software system
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3612 by runtime analysis performance monitoring G06F11/3466
• CPC Class Codes: G06F 11/3616 using software metrics
• CPC Class Codes: G06F 11/366 using diagnostics G06F11/0703 takes precedence
• CPC Class Codes: G06F 11/3664 Environments for testing or debugging software
• CPC Class Codes: G06F 11/3684 for test design, e.g. generating new test cases
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 11/3692 for test results analysis
• CPC Class Codes: G06F 2201/865 Monitoring of software

Abstract

Identification and dynamic ranking of performance issues. For an instance of a performance anti-pattern, identifying and recording information relating to a resultant performance issue, quantifying the magnitude of the performance issue, and dynamically ranking the performance issue against other performance issues.

Claims

1. A method comprising:

identifying a set of anti-patterns in a first application binary code;

creating a set of reports to describe a set of resultant impacts caused by the set of anti-patterns; and

ranking the set of anti-patterns based, at least in part, on a subset of the set of resultant impacts;

wherein;

at least the ranking step is performed by computer software running on computer hardware.

2. The method of claim 1, wherein creating a set of reports includes:

generating an execution log for the set of anti-patterns during execution of the first application binary code, including a set of timestamps;

generating a performance report for the set of resultant impacts during execution of the first application binary code; and

determining an association between a first anti-pattern in the set of anti-patterns and a first resultant impact in the set of resultant impacts, based at least in part on the execution log and the performance report.

3. The method of claim 2, further comprising:

executing a second application binary code to produce a second execution log and a second performance report; and

determining a performance pattern corresponding to a same anti-pattern in the set of anti-patterns, based at least in part on the first execution log, the first performance report, the second execution log, and the second performance report

4. The method of claim 3, further comprising:

predicting a resultant impact of the same anti-pattern identified in a third application binary code, based at least in part on the performance pattern.

5. The method of claim 1, wherein identifying the set of anti-patterns is performed as a static analysis.

6. The method of claim 1, wherein identifying the set of anti-patterns is performed as a runtime analysis.

7. The method of claim 1, further comprising:

classifying, based at least in part on the set of reports, each anti-pattern in the set of anti-patterns as one of;

on-effect, post-effect, or pre-effect.

8. The method of claim 1, wherein ranking the set of anti-patterns is based on a predicted quantitative resultant impact of each anti-pattern in the set of anti-patterns.

9. The method of claim 8, wherein the predicted quantitative resultant impact is based, at least in part, on a set of crowdsourced data.

10. The method of claim 1, wherein ranking the set of anti-patterns results in a visual output.

11. A computer program product comprising a computer readable storage medium having stored thereon:

first instructions executable by a device to cause the device to identify a set of anti-patterns in a first application binary code;

second instructions executable by a device to cause the device to create a set of reports to describe a set of resultant impacts caused by the set of anti-patterns; and

third instructions executable by a device to cause the device to rank the set of anti-patterns based, at least in part, on a subset of the set of resultant impacts.

12. The computer program product of claim 11, wherein the second instructions to create a set of reports includes:

fourth instructions executable by a device to cause the device to generate an execution log for the set of anti-patterns during execution of the first application binary code, including a set of timestamps;

fifth instructions executable by a device to cause the device to generate a performance report for the set of resultant impacts during execution of the first application binary code; and

sixth instructions executable by a device to cause the device to determine an association between a first anti-pattern in the set of anti-patterns and a first resultant impact in the set of resultant impacts, based at least in part on the execution log and the performance report.

13. The computer program product of claim 11, further comprising:

fourth instructions executable by a device to cause the device to classify, based at least in part on the set of reports, each anti-pattern in the set of anti-patterns as one of;

on-effect, post-effect, or pre-effect.

14. The computer program product of claim 11, wherein the third instructions to rank the set of anti-patterns is based on a predicted quantitative resultant impact of each anti-pattern in the set of anti-patterns.

15. The computer program product of claim 14, wherein the predicted quantitative resultant impact is based, at least in part, on a set of crowdsourced data.

16. A computer system comprising:

a processor set; and

a computer readable storage medium;

wherein;

the processor set is structured, located, connected, and/or programmed to execute instructions stored on the computer readable storage medium; and

the instructions include;

first instructions executable by a device to cause the device to identify a set of anti-patterns in a first application binary code;

second instructions executable by a device to cause the device to create a set of reports to describe a set of resultant impacts caused by the set of anti-patterns; and

third instructions executable by a device to cause the device to rank the set of anti-patterns based, at least in part, on a subset of the set of resultant impacts.

17. The computer system of claim 16, wherein the second instructions to create a set of reports includes:

fourth instructions executable by a device to cause the device to generate an execution log for the set of anti-patterns during execution of the first application binary code, including a set of timestamps;

fifth instructions executable by a device to cause the device to generate a performance report for the set of resultant impacts during execution of the first application binary code; and

sixth instructions executable by a device to cause the device to determine an association between a first anti-pattern in the set of anti-patterns and a first resultant impact in the set of resultant impacts, based at least in part on the execution log and the performance report.

18. The computer system of claim 16, further comprising:

fourth instructions executable by a device to cause the device to classify, based at least in part on the set of reports, each anti-pattern in the set of anti-patterns as one of;

on-effect, post-effect, or pre-effect.

19. The computer system of claim 16, wherein the third instructions to rank the set of anti-patterns is based on a predicted quantitative resultant impact of each anti-pattern in the set of anti-patterns.

20. The computer system of claim 19, wherein the predicted quantitative resultant impact is based, at least in part, on a set of crowdsourced data.