Methods and systems for derivation of missing data objects from test data

• US 20070179755A1
• Filed: 01/31/2006
• Published: 08/02/2007
• Est. Priority Date: 01/31/2006
• Status: Active Grant

• Current Assignee: Advantest Corporation
• Original Assignee: Verigy Limited (Advantest Corporation)
• Inventors: Connally, Carli, Carpenter, Bryan
• Granted Patent: US 7,328,137 B2
• Time in Patent Office: Days
• US Class Current: 702/189
• CPC Class Codes: G01R 31/3183 Generation of test inputs, e.g. test vectors, patterns or sequences
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99944 Object-oriented database structure

Abstract

In an embodiment, there is disclosed a system for derivation of missing data objects from test data. The system may include a data populator having code for: (1) generating data objects from the test data, (2) arranging the data objects in a tree structure, (3) deriving the missing data objects from the tree structure, and (4) populating the missing data objects into the tree structure; a data model in communication with the data populator; and a plurality of clients in communication with the data model. In an embodiment, a method of derivation of missing data objects from test data is disclosed. The method may include generating data objects from the test data; arranging the data objects in a tree structure; deriving the missing data objects from the tree structure; and populating the missing data objects into the tree structure. Other embodiments are also disclosed.

Claims

1. A system for derivation of missing data objects from test data, the system comprising:

a data populator having code for;

(1) generating data objects from the test data, (2) arranging the data objects in a tree structure, (3) deriving the missing data objects from the tree structure, and (4) populating the missing data objects into the tree structure;

a data model in communication with the data populator, wherein the data model stores the data objects and the missing objects generated by the data populator; and

a plurality of clients in communication with the data model, wherein the clients each selectively read the data objects and the missing data objects stored by the data model.

2. A system in accordance with claim 1, wherein the data objects and the missing data objects generated by the data populator form a uniform set of objects, wherein each one of the plurality of clients selectively reads from the uniform set of objects, and wherein different ones of the plurality of clients obtain standardized data from the uniform set of objects.

3. A system in accordance with claim 1, wherein the plurality of clients include data formatters.

4. A system in accordance with claim 2, wherein the data formatters include at least one chosen from the group consisting of an STDF writer, an ASCII writer, an XML writer, and an EDL writer.

5. A system in accordance with claim 1, wherein a notification manager is in communication with the data populator and the clients, wherein the notification manager notifies the clients of the data objects and missing data objects generated by the data populator, and wherein the clients each selectively read the data objects and missing data objects stored in the data model.

6. A system for derivation of missing data objects from test data, the system comprising:

a data populator having code for;

(1) generating data objects from the test data, (2) arranging the data objects in a relational structure relative to one another, (3) deriving the missing data objects from the relational structure, and (4) populating the missing data objects into the relational structure; and

a plurality of clients in communication with the data populator, wherein the clients each selectively obtain the data objects and the missing data objects generated and derived by the data populator, respectively.

7. A system in accordance with claim 6, wherein the data objects generated by the data populator and the missing data objects derived by the data populator form a uniform set of objects, wherein each one of the plurality of clients selectively obtains standardized data from the uniform set of objects.

8. A system in accordance with claim 6, wherein the plurality of clients include data formatters.

9. A system in accordance with claim 8, wherein the data formatters include at least one chosen from the group consisting of an STDF writer, an ASCII writer, an XML writer, and an EDL writer.

10. A system in accordance with claim 6, wherein a notification manager is in communication with the data populator and the clients, wherein the notification manager notifies the clients of the data objects and missing data objects generated by the data populator, and wherein the clients each selectively obtain the data objects and missing data objects stored in the data model.

11. A method of derivation of missing data objects from test data, the method comprising:

generating data objects from the test data;

arranging the data objects in a tree structure;

deriving the missing data objects from the tree structure; and

populating the missing data objects into the tree structure.

12. A method in accordance with claim 11, wherein generating data the objects from the test data occurs at a data populator having code therefor.

13. A method in accordance with claim 11, wherein arranging the data objects in a tree structure occurs at a data populator having code therefor.

14. A method in accordance with claim 11, wherein deriving the missing data objects from the tree structure occurs at a data populator having code therefor.

15. A method in accordance with claim 11, wherein populating the missing data objects into the tree structure occurs at a data populator having code therefor.

16. A method in accordance with claim 11, further comprising storing the data objects and the missing objects generated in a data model in communication with the data populator.

17. A method in accordance with claim 16, further comprising selectively reading the data objects and the missing data objects stored by the data model with a plurality of clients in communication with the data model.

18. A method of derivation of missing data objects from test data, the method comprising:

generating data objects from the test data;

arranging the data objects in a relational structure relative to one another;

deriving the missing data objects from the relational structure; and

populating the missing data objects into the relational structure.

19. A method in accordance with claim 18, wherein generating data the objects from the test data occurs at a data populator having code therefor.

20. A method in accordance with claim 18, wherein arranging the data objects in a relational structure occurs at a data populator having code therefor.

21. A method in accordance with claim 18, wherein deriving the missing data objects from the relational structure occurs at a data populator having code therefor.

22. A method in accordance with claim 18, wherein populating the missing data objects into the relational structure occurs at a data populator having code therefor.

23. A method in accordance with claim 18, further comprising storing the data objects and the missing objects generated in a data model in communication with the data populator.

24. A method in accordance with claim 23, further comprising selectively reading the data objects and the missing data objects stored by the data model with a plurality of clients in communication with the data model.

25. A method in accordance with claim 18, further comprising selectively reading the data objects and the missing data objects with a plurality of clients separate from a location for deriving the missing data objects from the relational structure.

26. A method of derivation of missing data objects from test data, the method comprising:

receiving an ordered series of events within the test data; and

creating missing data objects based on the ordered series events, wherein the ordered series of events skips the missing data objects, and wherein the missing data objects are expected within the ordered series of events within the test data.