Enabling troubleshooting of subroutines with greatest execution time/input data set size relationship

• US 6,058,265 A
• Filed: 10/21/1997
• Issued: 05/02/2000
• Est. Priority Date: 10/21/1997
• Status: Expired due to Fees

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Company (HP Inc.)
• Inventors: Bishop, John
• Primary Examiner(s): Downs, Robert W.
• Assistant Examiner(s): INGBERG, TODD D
• Application Number:
  US08/953,548
• Time in Patent Office: 924 Days
• Field of Search: 395/764, 395/704, 395/709, 395/708, 395/707, 395/706, 395/705, 395/703, 395/702, 395/701, 395/710, 395/712
• US Class Current: 717/127
• CPC Class Codes: G06F 11/3419 by assessing time
• CPC Class Codes: G06F 2201/88 Monitoring involving counting

Abstract

The method of the invention analyzes program components from a program to determine a set of subroutines whose execution time, for increasing input data set sizes, is predicted to increase at a rate that is more rapid than the rate exhibited by other subroutines in the program. The method of the invention includes the steps of: applying a plurality of different size data sets to the program; executing the program with each different size data set; determining execution times for each program component in the program for execution runs of the program; analyzing the execution times for each program component to determine a set of finite differences between execution times, during succeeding runs; and determining from sets of finite differences, which program components exhibit a largest growth in execution time, for increasing sizes of input data set size.

Claims

1. A method for analyzing a program to determine a set of components thereof whose execution times, for increasing input data set sizes applied to said program, are predicted to increase at a rate that is more rapid than other components of said program, said method comprising the steps of:

a) applying a plurality of different size data sets to said program;

b) executing said program with each of said different size data sets;

c) determining execution times for each said component of said program for each execution run of said program;

d) analyzing said execution times for each said component to determine a set of differences between succeeding execution times of succeeding runs; and

e) determining from sets of said differences, which components exhibit a largest growth of execution time in relation to increasing size data input sets to said program.

2. The method as recited in claim 1, further comprising the step of:

f) listing components which exhibit a like growth of execution time in relation to increasing size data input sets, into respective corresponding lists.

3. The method as recited in claim 1, wherein said different size data sets exhibit no preset relationship to a data set expected to be applied to said program during operational use thereof.

4. The method as recited in claim 1, wherein said analyzing step d) concludes when a new set of differences calculated from a previous set of differences approximate a zero value.

5. The method as recited in claim 4, wherein said analyzing step d) determines that said new set of differences calculated from said previous set of differences approximate zero values by comparing individual values of said new set of differences to a predetermined threshold.

6. The method as recited in claim 1, wherein said determining step e) finds which set of components exhibit a largest growth of execution time by assessing, for each component, a number of times a set of non-zero differences result from analyzing step d), and assigning components which exhibit a largest number of times value to said set of components that exhibit a largest growth of execution time.

7. A memory media for controlling a processor to analyze a program to determine a set of components thereof whose execution times, for increasing input data set sizes applied to said program, are predicted to increase at a rate that is more rapid than other components of said program, said memory media comprising:

a) means for controlling said processor to apply a plurality of different size data sets to said program;

b) means for controlling said processor to execute said program with each of said different size data sets;

c) means for controlling said processor to determine execution times for each said component of said program for each execution run of said program with said different size data sets;

d) means for controlling said processor to analyze said execution times for each said component to determine a set of differences between succeeding execution times of succeeding runs; and

e) means for controlling said processor to determine from sets of said differences, which components exhibit a largest growth of execution time in relation to increasing size data input sets to said program.

8. The memory media as recited in claim 7, further comprising:

f) means for controlling said processor to list components which exhibit a like growth of execution time in relation to increasing size data input sets, into respective corresponding lists.

9. The memory media as recited in claim 7, wherein said different size data sets exhibit no preset relationship to a data set expected to be applied to said program during operational use thereof.

10. The memory media as recited in claim 7, wherein said means d) concludes operation when a new set of differences calculated from a previous set of differences approximate a zero value.

11. The memory media as recited in claim 10, wherein said means d) determines that said new set of differences calculated from said previous set of differences approximate a zero value by comparing individual values of said new set of differences to a predetermined threshold value.

12. The memory media as recited in claim 7, wherein said means e) finds which set of components exhibit a largest growth of execution time by assessing, for each component, a number of times a set of non-zero differences result from analyzing step d), and assigning components which exhibit a largest number of times value to said set of components that exhibit a largest growth of execution time.