AUTOMATED LOAD MODEL

• US 20100115339A1
• Filed: 10/30/2008
• Published: 05/06/2010
• Est. Priority Date: 10/30/2008
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Hummel, David M. JR.
• Granted Patent: US 8,332,820 B2
• Time in Patent Office: Days
• US Class Current: 714/37
• CPC Class Codes: G06F 11/263 Generation of test inputs, e.g. test vectors, patterns or sequences ; with adaptation of the tested hardware for testability with external testers
• CPC Class Codes: G06F 11/3414 Workload generation, e.g. scripts, playback
• CPC Class Codes: G06F 11/3433 for load management allocation of a server based on load conditions G06F9/505; load rebalancing G06F9/5083; redistributing the load in a network by a load balancer H04L67/1029
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3672 Test management
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites

Abstract

A method and system for testing a computer system is provided. In one implementation the method and system may include receiving a script footprint that includes dimension-effect values corresponding to the number of times a computer system dimension is affected by the script. Target information may also be received. The target information includes target dimension values corresponding to a desired number of times per time period each dimension should be affected. The method and system may determining the number of times to execute the scripts within the time period so as to minimize the difference between the actual number of times dimensions are affected and the target-dimension value per time period. The method and system may also execute the script on the computer system the determined number of times within the time period.

Claims

1. A method for testing a computer system, the method comprising:

receiving script information that includes dimension-effect values, each dimension-effect value corresponding to a number of times a dimension is affected by a script;

receiving target-dimension values, each target-dimension value corresponding to a desired number of times per time period each dimension should be affected by the script;

determining a number of times to execute the script within the time period so as to minimize the difference between actual number of times dimensions are affected and the target-dimension value per time period; and

executing the script on the computer system the determined number of times within the time period.

2. The method according to claim 1, further comprising minimizing the difference between the actual number of times dimensions are affected by the script and the target-dimension value via a linear-least-squares algorithm.

3. The method according to claim 1, further comprising scaling the dimension-effect values and the target-dimension values by a weighting factor.

4. The method according to claim 3, wherein the weighting factor corresponds to the reciprocal of a highest of the target-dimension values.

5. The method according to claim 3, wherein the weighting factor corresponds to the reciprocal of an average of all of the target-dimension values.

6. The method according to claim 3, wherein the weighting factor corresponds to the reciprocal of a sum of all of the target-dimension values.

7. The method according to claim 1, further comprising multiplying the target-dimension values by a growth factor.

8. The method according to claim 1, further comprising converting the difference between actual number of times dimensions are affected and the target-dimension value into a percentage of error and communicating the percentage of error to a display.

9. A method for testing a computer system, the method comprising:

receiving script information that includes dimension-effect values for a plurality of scripts, each dimension-effect value corresponding to a number of times a dimension is affected by a script;

receiving target information that includes target-dimension values, each target-dimension value corresponding to a desired number of times per time period each dimension should be affected by the plurality of scripts;

determining a number of times to execute each of the plurality of scripts within the time period so as to minimize the difference between actual number of times dimensions are affected and the target-dimension value per time period; and

executing the plurality of scripts on the computer system the determined number of times within the time period.

10. A machine-readable storage medium having stored thereon a computer program comprising at least one code section for testing a computer system, the at least one code section being executable by a machine for causing the machine to perform acts of:

receiving script information that includes dimension-effect values, each dimension-effect value corresponding to a number of times a dimension is affected by a script;

receiving target-dimension values, each target dimension value corresponding to a desired number of times per time period each dimension should be affected by the script;

determining a number of times to execute the script within the time period so as to minimize the difference between actual number of times dimensions are affected and the target-dimension value per time period; and

executing the script on the computer system the determined number of times within the time period.

11. The machine-readable storage according to claim 10, wherein the code section is executable to cause the machine to minimize the difference between the actual number of times dimensions are affected by the script and the target-dimension value via a linear-least-squares algorithm.

12. The machine-readable storage according to claim 10, wherein the code section is executable to cause the machine to scale the dimension-effect values and the target-dimension values by a weighting factor.

13. The machine-readable storage according to claim 12, wherein the weighting factor corresponds to the reciprocal of a highest of the target-dimension values.

14. The machine-readable storage according to claim 12, wherein the weighting factor corresponds to the reciprocal of an average of all of the target-dimension values.

15. The machine-readable storage according to claim 12, wherein the weighting factor corresponds to the reciprocal of a sum of all of the target-dimension values.

16. The machine-readable storage according to claim 10, wherein the code section is executable to cause the machine to multiply the target-dimension values by a growth factor.

17. The machine-readable storage according to claim 10, wherein the code section is executable to cause the machine to convert the difference between actual number of times dimensions are affected and the target-dimension value into a percentage of error and communicating the percentage of error to a display.

18. A system for testing a computer system, the system comprising:

a processor operable to receive script information that includes dimension-effect values, each dimension-effect value corresponding to a number of times a dimension is affected by a script;

receive target-dimension values, each target dimension value corresponding to a desired number of times per time period each dimension should be affected by the script;

determine a number of times to execute the script within the time period so as to minimize the difference between the actual number of times dimensions are affected and the target-dimension value per time period; and

execute the script on the computer system the determined number of times within the time period.

19. The system according to claim 18, wherein the processor is operable to minimize the difference between the actual number of times dimensions are affected by the script and the target-dimension value via a linear-least-squares algorithm.

20. The system according to claim 18, wherein the processor is operable to scale the dimension-effect values and the target-dimension values by a weighting factor.

21. The system according to claim 20, wherein the weighting factor corresponds to the reciprocal of a highest of the target-dimension values.

22. The system according to claim 20, wherein the weighting factor corresponds to the reciprocal of an average of all of the target-dimension values.

23. The system according to claim 20, wherein the weighting factor corresponds to the reciprocal of a sum of all of the target-dimension values.

24. The system according to claim 18, wherein the processor is operable to multiply the target-dimension values by a growth factor.