Technologies for code failure proneness estimation

• US 8,332,822 B2
• Filed: 03/07/2011
• Issued: 12/11/2012
• Est. Priority Date: 04/26/2007
• Status: Expired due to Fees

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Nagappan, Nachiappan, Bhat, Thirumalesh
• Primary Examiner(s): Khatri, Anil
• Application Number:
  US13/042,404
• Publication Number:
  US 20110161932A1
• Time in Patent Office: 645 Days
• Field of Search: 717124-131, 717121-122
• US Class Current: 717/124
• CPC Class Codes: G06F 11/3616 using software metrics

Abstract

The present examples provide technologies for estimating code failure proneness probabilities for a code set and/or the files that make up the set. The code set being evaluated is typically comprised of binary and/or source files that embody the software for which the estimates are desired. The estimates are typically based on a set of selected code metrics, the code metrics typically selected based on corresponding failures of a previous version of the software. A historically variant metric feedback factor may also be calculated and code metric values classified relative to a baseline code set embodying the previous version of the software.

Claims

1. A method comprising:

calculating, by a computing device, churn metrics that indicate a degree of change between a first version of code and a second version of the code;

calculating, by the computing device for the second version and based on input code metrics that include the churn metrics, second metrics determined from the second version, and first metrics that correspond to the churn metrics and the second metrics that are determined from the first version, a historical variant metric feedback factor that is a sum divided by a total number of the input code metrics, where the sum is a count of each of the input code metrics that exceed a statistical upper bound that is based on the each of the input code metrics'"'"' corresponding first metric; and

performing, by the computing device and based on the second metrics or on the churn metrics, a logistical regression resulting in a code failure proneness probability for the second version.

2. The method of claim 1 wherein the churn metrics include M1 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to code in the first version.

3. The method of claim 2 wherein the churn metrics include M2 that represents contiguous code in the first version that is not in the second version, relative to code in the first version.

4. The method of claim 3 wherein the churn metrics include M3 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to contiguous code in the first version that is not in the second version.

5. The method of claim 4 wherein the code failure proneness probability for the second version is based on the performing the logistical regression using M1, M2, and M3.

6. The method of claim 5 further comprising determining for each of the first metrics its corresponding statistical upper bound.

7. At least one computer-readable storage medium storing computer-executable instructions that, when executed by a computer, cause the computer to perform a method comprising:

calculating churn metrics that indicate a degree of change between a first version of code and a second version of the code;

calculating, for the second version and based on input code metrics that include the churn metrics, second metrics determined from the second version, and first metrics that correspond to the churn metrics and the second metrics that are determined from the first version, a historical variant metric feedback factor that is a sum divided by a total number of the input code metrics, where the sum is a count of each of the input code metrics that exceed a statistical upper bound that is based on the each of the input code metrics'"'"' corresponding first metric; and

performing, based on the second metrics or on the churn metrics, a logistical regression resulting in a code failure proneness probability for the second version.

8. The at least one computer-readable storage medium of claim 7 wherein the churn metrics include M1 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to code in the first version.

9. The at least one computer-readable storage medium of claim 8 wherein the churn metrics include M2 that represents contiguous code in the first version that is not in the second version, relative to code in the first version.

10. The at least one computer-readable storage medium of claim 9 wherein the churn metrics include M3 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to contiguous code in the first version that is not in the second version.

11. The at least one computer-readable storage medium of claim 10 wherein the code failure proneness probability for the second version is based on the performing the logistical regression using M1, M2, and M3.

12. The at least one computer-readable storage medium of claim 11 further comprising determining for each of the first metrics its corresponding statistical upper bound.

13. A system comprising:

a computing device;

a code churn calculator implemented at least in part by the computing device and configured for calculating churn metrics that indicate a degree of change between a first version of code and a second version of the code;

a historical variant metric feedback calculator implemented at least in part by the computer and configured for calculating, for the second version and based on input code metrics that include the churn metrics, second metrics determined from the second version, and first metrics that correspond to the churn metrics and the second metrics that are determined from the first version, a historical variant metric feedback factor that is a sum divided by a total number of the input code metrics, where the sum is a count of each of the input code metrics that exceed a statistical upper bound that is based on the each of the input code metrics'"'"' corresponding first metric; and

a statistical modeler implemented at least in part by the computing device and configured for performing, based on the second metrics or on the churn metrics, a logistical regression resulting in a code failure proneness probability for the second version.

14. The system of claim 13 wherein the churn metrics include M1 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to code in the first version.

15. The system of claim 14 wherein the churn metrics include M2 that represents contiguous code in the first version that is not in the second version, relative to code in the first version.

16. The system of claim 15 wherein the churn metrics include M3 that represents new contiguous code in the second version that is not in the first version and contiguous code in the second version that has changed from that in the first version, relative to contiguous code in the first version that is not in the second version.

17. The system of claim 16 wherein the code failure proneness probability for the second version is based on the performing the logistical regression using M1, M2, and M3.

18. The system of claim 17 further comprising determining for each of the first metrics its corresponding statistical upper bound.