Process for making software diagnostics more efficient by leveraging existing content, human filtering and automated diagnostic tools

• US 20080126325A1
• Filed: 06/26/2007
• Published: 05/29/2008
• Est. Priority Date: 06/26/2006
• Status: Abandoned Application

• Current Assignee: Sourcelabs
• Original Assignee: Sourcelabs
• Inventors: Jekel, Ross Arden, Pugh, William, Hansson, Christian, Sweet, Ryan, Ruan, Yongshao, Jacobson, Steve
• Application Number:
  US11/768,346
• Publication Number:
  US 20080126325A1
• Time in Patent Office: Days
• US Class Current: 707/4
• CPC Class Codes: G06F 11/36 Preventing errors by testing or debugging software

Abstract

Improved methods of software diagnostics are provided. Searches of data sources are conducted using search terms from internal computer information to obtain searched data. The searched data is processed by extracting technical features. The technical features are indexed to create indexes that can be searched via machine state. Filtering is conducted over the gathered data to create feeds that are available to customers. Stack traces are extracted for the purpose of matching and researching using machine state rather than key words.

Claims

1. A method of performing software diagnostics, comprising:

conducting searches of data sources using search terms from internal computer information to obtain searched data,

processing the searched data by extracting technical features;

indexing the technical features to create indexes that can be searched via machine state,

using filtering over the gathered data to create feeds that are available to customers; and

extracting stack traces for the purpose of matching and researching using machine state rather than key words.

2. The method of claim 1, wherein dictionaries are build in part from on code related artifacts.

3. The method of claim 1, further comprising:

performing web scraping on at least security related sites.

4. The method of claim 1, further comprising:

using compiler tools to create the dictionaries; and

creating from the dictionaries a hash table or a suffix tree.

5. The method of claim 1, further comprising:

ranking the searched data using heuristics.

6. The method of claim 5, wherein the searched data is from mail threads.

7. The method of claim 6, further comprising:

determining a number of responses in the mail thread.

8. The method of claim 6, further comprising:

determining authorship of the mail threads.

9. The method of claim 6, further comprising:

determining if the mail thread is from a code check-in event.

10. The method of claim 6, further comprising:

determining if the mail thread is from a bug activity event.

11. The method of claim 5, wherein the searched data is from a bug.

12. The method of claim 11, further comprising:

determining how the bug was resolved.

13. The method of claim 11, further comprising:

determining who opened the bug.

14. The method of claim 11, further comprising:

determining who resolved the bug.

15. The method of claim 11, further comprising:

determining if the bug is fixed.

16. The method of claim 11, further comprising:

determining how long the bug has remained untouched by a software community.

17. The method of claim 5, wherein the searched data is code.

18. The method of claim 17, further comprising:

determining how check-in of the code effected code complexity.

19. The method of claim 17, further comprising:

determining an amount of churn that is created by check-in of the code.

20. The method of claim 17, further comprising:

determining how check-in of the code is related to a bug.