The document discusses how task granularity at different levels (e.g. commits, pull requests, work items) can impact analyses of co-evolution in software projects. It finds that analyzing at the commit-level can overlook relationships between tasks that span multiple commits. Work item level analysis is recommended to provide a more complete view of co-evolution, as median of 29% of work items consist of multiple commits, and analyzing at the commit level would miss 24% of co-changed files and inability to group 83% of related commits.

1. The Impact of Task Granularity on
Co-evolution Analyses
Yasutaka
Kamei
Keisuke
Miura
Shane
McIntosh
Naoyasu
Ubayashi
Ahmed E.
Hassan

2. Software evolution aims to recover
knowledge about development
2
Repositories Knowledge
GerritGit GitHub
RHSA Mylyn

3. Co-evolution of production &
test code
3
Growth history view of ArgoUML [1]
[1] A. Zaidman, B. Van Rompaey, S. Demeyer, and A. Van Deursen. Mining software repositories to study co-evolution of production & test code.
In Proc. Int’l Conf. on Software Testing, Verification, and Validation (ICST’08), pages 220–229, 2008.

4. There are several levels of
granularity
4

5. There are several levels of
granularity
4
Commits

6. There are several levels of
granularity
4
Pull
Requests
Merge
Commits

7. There are several levels of
granularity
4
Pull
Requests
Merge v2.0
ReleasesCommits

8. Some issues may require several
commits
5
Fix #1000
A
Test
A

9. Fix #1100
B
Some issues may require several
commits
5
Fix #1000
A
Test
A

10. Fix #1100
B
Some issues may require several
commits
5
Fix #1000
A
Test
A
Fix #1100
Test
B
1 day
later

11. Fix #1100
B
Some issues may require several
commits
5
Fix #1000
A
Test
ACommit-level analysis would miss the
co-change relationship between them
Fix #1100
Test
B
1 day
later

12. Work items can be used to study
software evolution
6
Jira
#1000 #1100

13. Jira
#1000 #1100
Work items can be used to study
software evolution
7

14. Git
Jira
#1000 #1100
Fix #1000
A
Test
A
B
Test
B
Fix #1100Fix #1100
Work items can be used to study
software evolution
8

15. Git
Jira
#1000 #1100
Fix #1000
A
Test
A
B
Test
B
Fix #1100Fix #1100
Work items can be used to study
software evolution
9

16. Git
Jira
#1000 #1100
Fix #1000
A
Test
A
B
Test
B
Fix #1100Fix #1100
How does the work item
granularity impact co-
evolution analyses?
Work items can be used to study
software evolution
10

17. Studied systems
11

18. Studied systems
11
Jira

19. Studied systems
11
Jira
119 systems

20. 12
System size ITS usage
Two important criteria that needed to
be satisfied to qualify for our analysis

21. 12
System size ITS usage
Two important criteria that needed to
be satisfied to qualify for our analysis
Jira

22. 13
System size ITS usage
100
75
50
25
0 10,000 20,000
# of commits
Two important criteria that needed to
be satisfied to qualify for our analysis

23. 13
System size ITS usage
100
75
50
25
0 10,000 20,000
# of commits
Two important criteria that needed to
be satisfied to qualify for our analysis
Jira

24. Studied systems
14

25. 15
How often are work items
composed of several commits?

26. 15
Git
Jira
#1000
Bug Fix
#1100
BA
Fix #1000
#1100
C
#1200
Fix a Bug
#1200
How often are work items
composed of several commits?

27. 16
Median of 29% of work items
consist of two or more commits

28. 16
Median of 29% of work items
consist of two or more commits
Granularity may have a
considerable impact on
co-evolution analyses

29. 17
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread

30. 17
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread

31. 18
How many files are changed by
the commits of work items?
Git
#1000
A
Test
A
A2 A3
Fix #1000 Fix #1000 Fix #1000

32. 18
How many files are changed by
the commits of work items?
Git
#1000
A
Test
A
A2 A3
Fix #1000 Fix #1000 Fix #1000

33. 19
OpenJPA #1763:
An example of file spread

34. 19
OpenJPA #1763:
An example of file spread

35. 19
OpenJPA #1763:
An example of file spread

36. 19
OpenJPA #1763:
An example of file spread

37. 19
OpenJPA #1763:
An example of file spread
60% (= 3 / 5)

38. 20
First-commit analysis would overlook
24% of the co-changed files (median)

39. 21
QPID-4575: adds support for
Visual Studio 2012
Git
1st commit 5th commit
.cpp .h .cpproj

40. 21
QPID-4575: adds support for
Visual Studio 2012
Git
1st commit 5th commit
.cpp .h
This co-change activity of
production code and build
system would be missed
.cpproj

41. 22
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked

42. 22
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
How much time elapses
between the commits
of work items?

43. Sliding time window technique
A common setting in software evolution
studies
23
Same commit message
Same developer
Similar time (300 secs)

44. Sliding time window technique
A common setting in software evolution
studies
23
A
Test
A
Fix #1000 Fix #1000
Git
Same commit message
Same developer
Similar time (300 secs)

45. Sliding time window technique
A common setting in software evolution
studies
23
A
Test
A
Fix #1000 Fix #1000
Git
< 300 secs
Same commit message
Same developer
Similar time (300 secs)

46. An example of time spread
24
R1 R2 R3 R4
Jira
#1000

47. An example of time spread
24
R1 R2 R3 R4
250 secs 200 secs 400 secs
Jira
#1000

48. An example of time spread
24
R1 R2 R3 R4
250 secs 200 secs 400 secs
33% (= 1 / 3)
Jira
#1000

49. 25
48%-97% of related commits cannot
be grouped using the sliding window

50. ACCUMULO-1890
Clean up the test
to avoid spinning
up a MAC
26
ACCUMULO-1890: recovers from a
failure due to limited resources

51. ACCUMULO-1890
Clean up the test
to avoid spinning
up a MAC
26
11 minutes later
ACCUMULO-1890: recovers from a
failure due to limited resources

52. ACCUMULO-1890
Clean up the test
to avoid spinning
up a MAC
26
ACCUMULO-1890
Forgot to re-add
changes before
commit
11 minutes later
ACCUMULO-1890: recovers from a
failure due to limited resources

53. 27
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
83% of related
commits cannot
be grouped

54. 27
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
83% of related
commits cannot
be grouped
How many developers
are involved across
revisions of a work item?

55. Sliding time window technique
A common setting in software evolution
studies
28
A
Test
A
Fix #1000 Fix #1000
Git
< 300 secs
Same commit message
Same developer
Similar time (300 secs)

56. 29
How many developers are involved
across revisions of a work item?

57. 29
How many developers are involved
across revisions of a work item?
Jira
#1000

58. 29
How many developers are involved
across revisions of a work item?
Jira
#1000
Git
Bug Fix
#1000
BA
Fix #1000
C
Fix a Bug
#1000

59. 29
How many developers are involved
across revisions of a work item?
Jira
#1000
Git
Bug Fix
#1000
BA
Fix #1000
C
Fix a Bug
#1000

60. 30
25% of work items involve
multiple developers

61. 31
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
83% of related
commits cannot
be grouped
25% of work
items involve
multiple
developers

62. 32
[2]Q. Xuan and V. Filkov. Building it together: Synchronous development in OSS. In Proc. Int’l Conf. on Software Engineering (ICSE’14), pages 222–233, 2014.
A set of commits where one file is modified
by multiple developers within a time window
Synchronous development [2]

63. 32
[2]Q. Xuan and V. Filkov. Building it together: Synchronous development in OSS. In Proc. Int’l Conf. on Software Engineering (ICSE’14), pages 222–233, 2014.
A set of commits where one file is modified
by multiple developers within a time window
A
Synchronous development [2]

64. 33
[2]Q. Xuan and V. Filkov. Building it together: Synchronous development in OSS. In Proc. Int’l Conf. on Software Engineering (ICSE’14), pages 222–233, 2014.
A set of commits where different files are
modified by multiple developers under the
same work item
A
Test
A
#1000
Collaborative development

65. 34
Collaborative development
[2]Q. Xuan and V. Filkov. Building it together: Synchronous development in OSS. In Proc. Int’l Conf. on Software Engineering (ICSE’14), pages 222–233, 2014.
A set of commits where different files are
modified by multiple developers under the
same work item
A
Test
A
#1000
We investigate collaborative
work items that cannot be
detected as synchronous ones

66. This type of collaboration is not rare
27%-83% of collaborative work items
involve developers modifying different files
35

67. Conclusion
36

69. Median of 29% of work items
consist of two or more commits
Granularity may have a
considerable impact on
co-evolution analyses

70. Median of 29% of work items
consist of two or more commits
Granularity may have a
considerable impact on
co-evolution analyses
Studied systems

71. Median of 29% of work items
consist of two or more commits
Granularity may have a
considerable impact on
co-evolution analyses
Studied systems
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
83% of related
commits cannot
be grouped
25% of work
items involve
multiple
developers

72. Median of 29% of work items
consist of two or more commits
Granularity may have a
considerable impact on
co-evolution analyses
Studied systems
The impact of the work item
granularity
File
Spread
Time
Spread
Developer
Spread
24% of the co-
changed files
are overlooked
83% of related
commits cannot
be grouped
25% of work
items involve
multiple
developers
Given the impact that work item grouping,
we recommend that future software
evolution studies will be performed at the
work item level.

73. 39
Back up slides

74. Work item aggregation
40
Git
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2.2.0 release
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