Method and apparatus for processing information on software defects during computer software development

• US 7,401,321 B2
• Filed: 04/14/2003
• Issued: 07/15/2008
• Est. Priority Date: 04/14/2003
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sit, Steven, DeKimpe, Daniel, Phung, Tamuyen
• Primary Examiner(s): Dam; Tuan Q.
• Assistant Examiner(s): DAO, THUY CHAN
• Application Number:
  US10/413,240
• Publication Number:
  US 20040205727A1
• Time in Patent Office: 1,919 Days
• Field of Search: 717/128, 717/125, 717/100, 717/105, 709/223, 715/503, 707/3
• US Class Current: 717/125
• CPC Class Codes: G06F 11/3664 Environments for testing or debugging software

Abstract

An apparatus for processing data relating to software defects operates within an on-line analytical processing environment that includes an on-line analytical processing presentation tool (60). An on-line analytical processing cube model builder (32) builds a plurality of interrelated tables (22) with on-line analytical processing cube model metadata (30). The cube model (30) is configured to be processed by the associated on-line analytical processing presentation tool (60). A data extraction tool (36) communicates with a defect tracking database (10, 12) containing said data relating to software defects. The data extraction tool (36) extracts said data from the defect tracking database (10, 12), transforms the extracted data into the software defect entries, and loads the software defect entries into the tables (22).

Claims

1. An apparatus including a program storage medium readable by a computer for processing data relating to software defects, the apparatus operative within an associated on-line analytical processing environment that includes at least an on-line analytical processing presentation tool for presenting results of on-line analytical processing, the apparatus comprising:

an on-line analytical processing cube model builder for building a plurality of interrelated tables with on-line analytical processing cube model metadata, the on-line analytical processing cube model metadata defining a cube model that represents a structural relationship between said plurality of interrelated tables, and the metadata including at least one on-line analytical processing metadata object defining a measurement entity operative in conjunction with a dimensional context, the measurement entity including at least one of a defect count measure defined in eXtensible Markup Language (XML) and a defect count average measure defined in XML, the tables including at least a defect facts table containing software defects entries corresponding to said data relating to software defects and a plurality of dimension tables, the defect facts table being interrelated with the plurality of dimension tables by a facts and dimensions table builder, the cube model including the software defects entries being configured to be processed by the associated on-line analytical processing presentation tool by means of an on-line analytical processing cube generated from said software defects entries in said cube model based on selected metadata in the plurality of dimension tables, said metadata categorizing at least one set of related attributes that together describe an aspect of at least one dimension measure corresponding to said software defects entries; and

a data extraction tool communicating with a defect tracking database containing said data relating to software defects, the data extraction tool extracting said data from the defect tracking database, transforming the extracted data into the software defect entries, and loading the software defect entries into the defect facts table.

2. The apparatus as set forth in claim 1, wherein the plurality of dimension tables include:

a time dimension table storing year, month, and day dimensional data and at least one of week dimensional data and quarter dimensional data.

3. The apparatus as set forth in claim 2, wherein the fact table is joined to the time dimension table.

4. The apparatus as set forth in claim 1, wherein:

the data extraction tool is adapted to communicate with a project management tool to extract data pertaining to identified software defects therefrom; and

the data data extraction tool is adapted to incorporate the data extracted from the project management tool into the software defect entries.

5. The apparatus as set forth in claim 1, further including:

an on-line analytical processing cube builder accessing the on-line analytical processing cube model metadata and constructing said on-line analytical processing cube having selected dimensions from the cube model.

6. The apparatus as set forth in claim 5, wherein the on-line analytical processing cube builder is integrated with the presentation tool.

7. The apparatus as set forth in claim 1, wherein:

the data extraction tool is responsive to a data refresh trigger to update software defect entries in the defects fact table and metadata in the on-line analytical processing cube model at selected times.

8. The apparatus as set forth in claim 1, wherein the on-line analytical processing cube model builder includes:

a tables builder for building the defects fact table and the plurality of dimension tables; and

a dimensional data loader for loading dimensional data into the dimension tables.

9. The apparatus as set forth in claim 8, wherein the on-line analytical processing cube model builder further includes:

a dimensional data generator for generating at least some of the dimensional data loaded by the dimensional data loader.

10. The apparatus as set forth in claim 9, wherein the dimensional data generator outputs a text file containing dimensional data.

11. The apparatus as set forth in claim 1, wherein the on-line analytical processing cube model metadata conforms with a standardized general purpose metadata specification, and the on-line analytical processing presentation tool is operable with the standardized general purpose metadata specification.

12. The apparatus as set forth in claim 1, wherein

said defect count measure is defined in XML as:

−

<

measure name=“

DEFECT COUNT”

schema=“

DEFECT”

BusinessName=“

DEFECT COUNT”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

mm;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

−

<

sqlExpression template=“

{$$1}”

>

<

column name=“

AGE”

tableSchema=“

DEFECT”

tableName=“

DEFECTFACT”

/>

<

/sqlExpression>

<

aggregation function=“

COUNT”

/>

<

/measure>

where tableSchema=“

DEFECT”

references said interrelated tables and tableName=DEFECTFACT references said defect facts table.

13. The apparatus as set forth in claim 1, wherein said defect count average measure is defined in XML as:

−

<

measure name=“

DEFECT COUNT AVERAGE”

schema=“

DEFECT”

businessName=“

DEFECT COUNT AVERAGE”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

min;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

<

sqlExpression template=“

1”

/>

<

aggregation function=“

COUNT”

>

<

dimensionRef name=“

Variable1”

schema=“

DEFECT”

/>

.

.

.

<

dimensionRef name=“

VariableN”

schema=“

DEFECT”

/>

<

/aggregation>

<

aggregation function=AVG”

/>

<

/measure>

where schema=“

DEFECT”

references said interrelated tables and dimensionRef name=“

Variable1”

. . . dimensionRef name=“

VariableN”

references said defect facts table.

14. The apparatus as set forth in claim 1, wherein the at least one on-line analytical processing object is operative to perform at least a count of the number of software defects entries in at least one dimension and an average of the number of software defects.

15. The apparatus as set forth in claim 1, wherein the facts and dimensions table builder is described via a series of data definition language (DDL) commands.

16. The apparatus as set forth in claim 1, wherein said measurement entity operative in conjunction with said dimensional context includes at least one of an estimated duration measure, a days completed measure, a task completed measure, a remaining effort in days measure, and a defect count by weeks measure.

17. A method for processing data on software, the method comprising:

formatting information pertaining to identified software defects and loading the formatted information as defect entries into a defects database;

constructing a multidimensional on-line analytical processing cube model associated with the defects database, the cube model including metadata for at least a plurality of time dimensions, and including at least one on-line analytical processing metadata object defining a measurement entity operative in conjunction with a dimensional context, the measurement entity including at least one of a defect count measure defined in XML and a defect count average measure defined in XML;

constructing an on-line analytical processing cube based on the defects database, the cube model, and cube dimensions selected from the dimensions included in the cube model; and

accessing the on-line analytical processing cube using an on-line analytical processing presentation tool.

18. The method as set forth in claim 17, wherein the constructing of an on-line analytical processing cube includes:

for at least one selected cube dimension, selecting a hierarchy of the selected dimension.

19. The method as set forth in claim 17, wherein the constructing of a multidimensional on-line analytical processing cube model includes:

generating a plurality of tables including at least a fact table and a plurality of dimension tables;

joining at least some columns of the fact table with dimension tables; and

loading dimensional data into the dimension tables.

20. The method as set forth in claim 19, wherein the constructing of a multidimensional on-line analytical processing cube model further includes:

generating at least some of the dimensional data algorithmically.

21. The method as set forth in claim 17, wherein the accessing of the on-line analytical processing cube using an on-line analytical processing presentation tool includes:

selecting dimensions of the cube for display; and

displaying a tabulation of cube contents, the tabulation having the selected dimensions.

22. The method as set forth in claim 17, wherein the accessing of the on-line analytical processing cube using an on-line analytical processing presentation tool includes:

selecting one of the cube dimensions as an abscissa dimension; and

plotting a measure of the on-line analytical processing cube versus the abscissa dimension.

23. The method as set forth in claim 17, further including:

prior to the formatting of information pertaining to identified software defects, extracting the information pertaining to identified software defects from a defect tracking database.

24. The method as set forth in claim 17, further including:

prior to the formatting of information pertaining to identified software defects, extracting the information pertaining to identified software defects from at least one of a defect tracking database and a project management tool.

25. The method as set forth in claim 17, wherein the formatting of information pertaining to identified software defects and loading the formatted information as defect entries into a defects database includes:

associating an extract date with each defect entry, the extract date indicating when the defect entry was read from an associated data source.

26. The method as set forth in claim 25, wherein the constructing of an on-line analytical processing cube based on the defects database, the cube model, and a selection of cube dimensions includes:

selecting the extract date as a cube dimension.

27. The method as set forth in claim 26, wherein the accessing of the on-line analytical processing cube using an on-line analytical processing presentation tool includes:

slicing, dicing, or drilling through the on-line analytical processing cube using the extract date dimension to present a temporal snapshot of the defects data.

28. The method as set forth in claim 17, wherein said defect count measure is defined in XML as:

−

<

measure name=“

DEFECT COUNT”

schema=“

DEFECT”

BusinessName=“

DEFECT COUNT”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

mm;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

−

<

sqlExpression template=“

{$$1}”

>

<

column name=“

AGE”

tableSchema=“

DEFECT”

tableName=“

DEFECTFACT”

/>

<

/sqlExpression>

<

aggregation function=“

COUNT”

/>

<

/measure>

where tableSchema=“

DEFECT”

references a plurality of interrelated tables of said defects database, and

tableName=DEFECTFACT references a defect facts table of said plurality of interrelated tables.

29. The method as set forth in claim 17, wherein said defect count average measure is defined in XML as:

−

<

measure name=“

DEFECT COUNT AVERAGE”

schema=“

DEFECT”

businessName=“

DEFECT COUNT AVERAGE”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

min;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

<

sqlExpression template=“

1”

/>

<

aggregation function=“

COUNT”

>

<

dimensionRef name=“

Variable1”

schema=“

DEFECT”

/>

.

.

.

<

dimensionRef name=“

VariableN”

schema=“

DEFECT”

/>

<

/aggregation>

<

aggregation function=AVG”

/>

<

/measure>

where schema=“

DEFECT”

references a plurality of interrelated tables of said defects database, and

dimensionRef name=“

Variable1”

. . . dimensionRef name=“

VariableN”

references a defect facts table of said plurality of interrelated tables.

30. The method as set forth in claim 17, wherein the at least one on-line analytical processing objects is operative to perform at least a count of the number of software defects entries in at least one dimension and an average of the number of software defects.

31. The method as set forth in claim 17, wherein the constructing a multidimensional on-line analytical processing cube model further comprises describing a facts and dimensions table builder via a series of data definition language (DDL) commands.

32. The method according to claim 17, wherein said constructing includes constructing the cube model and said measurement entity includes at least one of an estimated duration measure, a days completed measure, a task completed measure, a remaining effort in days measure, and a defect count by weeks measure.

33. An article of manufacture comprising a program storage medium readable by a computer and embodying one or more instructions executable by the computer to perform a method for processing data on identified software defects, the method comprising:

constructing a multidimensional on-line analytical processing cube model representing a structural relationship between a plurality of interrelated tables, the cube model including at least a plurality of time dimensions and at least one on-line analytical processing metadata object defining a measurement entity operative in conjunction with a dimensional context, the measurement entity including at least one of a defect count measure defined in XML and a defect count average measure defined in XML;

generating a multidimensional on-line analytical processing cube having dimensions containing information pertaining to identified software defects, the cube generated based on selected metadata in the on-line analytical processing cube model categorizing at least one set of related attributes that together describe an aspect of at least one of said dimensions; and

accessing the on-line analytical processing cube using an on-line analytical processing presentation tool.

34. The article of manufacture as set forth in claim 33, wherein the generating of an on-line analytical processing cube includes:

formatting information pertaining to identified software defects and storing the formatted information as defect entries associated with the cube model; and

constructing the on-line analytical processing cube based on the stored defect entries, the cube model, and selected cube dimensions.

35. The article of manufacture as set forth in claim 34, wherein the formatting of information pertaining to identified software defects and storing the formatted information as defect entries associated with the cube model includes:

extracting the information pertaining to identified software defects at selected times; and

during the formatting of defect entries, tagging each defect entry with at least one of an extraction date value and an extraction time value.

36. The article of manufacture as set forth in claim 35, wherein the selected cube dimensions of the on-line analytical processing cube include an extraction time dimension, and the accessing of the on-line analytical processing cube using an on-line analytical processing presentation tool includes:

plotting a cube dimension other than the extraction time dimension against the extraction time dimension.

37. The article of manufacture as set forth in claim 33, wherein the accessing of the on-line analytical Processing cube includes:

communicating selected portions of said information pertaining to the identified software defects from the on-line analytical processing presentation tool to a spreadsheet program; and

displaying one of a tabulation and a plot of the communicated selection portions using the spreadsheet program.

38. The article of manufacture as set forth in claim 33, wherein said defect count measure is defined in XML as:

−

<

measure name=“

DEFECT COUNT”

schema=“

DEFECT”

BusinessName=“

DEFECT COUNT”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

mm;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

−

<

sqlExpression template=“

{$$1}”

>

<

column name=“

AGE”

tableSchema=“

DEFECT”

tableName=“

DEFECTFACT”

/>

<

/sqlExpression>

<

aggregation function=“

COUNT”

/>

<

/measure>

where tableSchema=“

DEFECT”

references said interrelated tables and dimensionRef=“

Variable1”

. . . dimensionRef=“

VariableN”

references a defect facts table of the interrelated tables.

39. The article of manufacture as set forth in claim 33, wherein said defect count average measure is defined in XML as:

<

measure name=“

DEFECT COUNT AVERAGE”

schema=“

DEFECT”

businessName=“

DEFECT COUNT AVERAGE”

createTime=“

year-mo-date T hr;

min;

sec”

creator=“

ARBOR”

modifyTime=“

year-mo-date T hr;

min;

sec”

modifier=“

ARBOR”

>

<

datatype schema=“

SYSIBM”

name=“

INTEGER”

length=“

4”

scale=“

0”

/>

<

sqlExpression template=“

1”

/>

<

aggregation function=“

COUNT”

>

<

dimensionRef name=“

Variable1”

schema=“

DEFECT”

/>

.

.

.

<

dimensionRef name=“

VariableN”

schema=“

DEFECT”

/>

<

/aggregation>

<

aggregation function=AVG”

/>

<

/measure>

where schema=“

DEFECT”

references said interrelated tables and dimensionRef name=“

Variable1”

. . . dimensionRef name=“

VariableN”

references a defect facts table of said plurality of interrelated tables.

40. The article of manufacture as set forth in claim 33, wherein the at least one on-line analytical processing object is operative to perform at least a count of the number of software defects entries in at least one dimension and an average of the number of software defects.

41. The article of manufacture as set forth in claim 33, wherein the method for processing data on identified software defects further comprises describing a facts and dimensions table builder via a series of data definition language (DDL) commands.

42. The article of manufacture as set forth in claim 33 further comprising constructing said cube model including said metadata object comprising at least one of an estimated duration measure, a days completed measure, a task completed measure, a remaining effort in days measure, and a defect count by weeks measure.