The document presents a method for predicting subsystem failures in software projects using dependency graph complexities. It finds that code dependency cycles and large cliques (fully connected subgraphs) correlate with more defects. It also finds that measures of complexity in a subsystem's dependency graph, such as the number of nodes and edges, can be used to predict the number of defects in the subsystem. Evaluating the method on a large Microsoft software project showed it could effectively rank subsystems by likelihood of failure.

1. Predicting Subsystem Failures
using Dependency Graph Complexities
Thomas Zimmermann, University of Calgary, Canada
Nachiappan Nagappan, Microsoft Research, USA

2. Predicting Subsystem Defects
using Dependency Graph Complexities
search: ISSRE
Thomas Zimmermann, University of Calgary, Canada
Nachiappan Nagappan, Microsoft Research, USA

4. Bugs are everywhere

5. Bugs are everywhere

6. Bugs are everywhere

7. Quality assurance is limited...
...by time...

8. Quality assurance is limited...
...by time... ...and by money.

9. Resource allocation
Spent resources on the
components that need it most,
i.e., are most likely to fail.

10. Meet Jacob
• Your QA manager
• Ten years knowledge
of your project
• Aware of its history
and the hot spots

11. Meet Jacob
• Your QA manager
• Ten years knowledge
of your project
• Aware of its history
and the hot spots
• Likes extreme sports

12. Meet Emily
• Your new QA manager
(replaces Jacob)
• Not much experience
with your project yet
• How can she allocate
resources effectively?

13. Meet Emily
• Your new QA manager
(replaces Jacob)
• Not much experience
with your project yet
• How can she allocate
resources effectively?

14. Indicators of failures
 Code complexity
◦ Basili et al. 1996, Subramanyam and Krishnan 2003,
◦ Binkley and Schach 1998, Ohlsson and Alberg 1996,
◦ Nagappan et al. 2006
 Code churn
◦ Nagappan and Ball 2005
 Historical data
◦ Khoshgoftaar et al. 1996, Graves et al. 2000, Kim et al. 2007,
◦ Ostrand et al. 2005, Mockus et al. 2005
 Code dependencies
◦ Nagappan and Ball 2007

15. Windows Server 2003

16. Windows Server 2003
 2254 Binaries
 28.4 MLOC

17. What are dependencies?
 Dependency = (directed)
relationship between two pieces of code

18. What are dependencies?
 Dependency = (directed)
relationship between two pieces of code
 MaX dependency analysis framework
◦ Caller-callee dependencies
◦ Imports and exports
◦ RPC
◦ COM
◦ Runtime dependencies (such as LoadLibrary)
◦ Registry access
◦ etc.

19. Windows Server layout

20. Windows Server layout

21. Windows Server layout

22. Windows Server layout

23. Complexity of subsystems
Subsystem A

24. Complexity of subsystems
Subsystem A Subsystem B

25. Complexity of subsystems
Subsystem A Subsystem B
Which subsystem has more defects?

26. Complexity of subsystems
Subsystem A Subsystem B
Which subsystem has more defects?
Our hypothesis: the more complex one.

27. Observation #1: Cycles
Dependency cycles:
No dependency cycle:

28. Observation #1: Cycles
Dependency cycles:
No dependency cycle:
Binaries that are part of a dependency cycle
have on average twice as many defects.

29. Observation #2: Cliques

30. Observation #2: Cliques

31. Observation #2: Cliques
Average number of defects is
higher for binaries in large cliques.

32. Data collection

33. Data collection

34. Data collection
defects
Defects

35. Dependency graphs
What is the dependency
graph of a subsystem?

36. Dependency graphs
INTRA
=Internal dependencies

37. Dependency graphs
OUT
=Outgoing dependencies

38. Dependency graphs
DEP
=“Neighborhood”
=INTRA + OUT + more

39. Complexity measures
#Nodes |V| Multiplicity
Complexity #Edges |E|
|E|-|V|+|P| Degree
Density
|E|/|V|2
Eccentricity
Radius Diameter

40. Spearman correlations

41. Spearman correlations
Complexity Measures

42. Spearman correlations
Dependency Graphs
Complexity Measures

43. Spearman correlations
Dependency Graphs
Complexity Measures

44. Spearman correlations
Dependency Graphs
Complexity Measures

45. Spearman correlations
Dependency Graphs
Complexity Measures

46. Spearman correlations
Dependency Graphs
Complexity Measures

47. Predicting failures
NODES
EDGES
COMPLEXITY
DENSITY
DEGREE_MIN
DEGREE_MAX
DEGREE_AVG
ECCENTRICITY_MIN
ECCENTRICITY_MAX
ECCENTRICITY_AVG
MULTI_EDGES
MULTI_COMPLEXITY
MULTI_DENSITY
MULTI_DEGREE_MIN
MULTI_DEGREE_MAX
MULTI_DEGREE_AVG
MULTI_MULTIPLICITY_MIN
MULTI_MULTIPLICITY_MAX
MULTI_MULTIPLICITY_AVG
MULTI_ECCENTRICITY_MIN
MULTI_ECCENTRICITY_MAX
MULTI_ECCENTRICITY_AVG

48. Predicting failures
NODES
EDGES
COMPLEXITY
DENSITY
DEGREE_MIN
DEGREE_MAX
DEGREE_AVG
ECCENTRICITY_MIN
ECCENTRICITY_MAX
ECCENTRICITY_AVG
MULTI_EDGES
MULTI_COMPLEXITY
MULTI_DENSITY
INTRA
MULTI_DEGREE_MIN
MULTI_DEGREE_MAX OUT
MULTI_DEGREE_AVG
MULTI_MULTIPLICITY_MIN
MULTI_MULTIPLICITY_MAX
DEP
COMBINED
MULTI_MULTIPLICITY_AVG
MULTI_ECCENTRICITY_MIN
MULTI_ECCENTRICITY_MAX
MULTI_ECCENTRICITY_AVG

49. Ranking

50. Ranking
Rank Subsystem Actual Rank
1 K 3
2 L 95
3 C 6
4 G 2
5 F 8
6 A 3
7 Y 12
8 O 1
9 B 18
10 M 35
... (many more)

51. Ranking
Rank Subsystem Actual Rank
1 K 3
2 L 95
3 C 6
4 G 2
5 F 8
6 A 3
7 Y 12
8 O 1
9 B 18
10 M 35
... (many more)

52. Ranking
Rank Subsystem Actual Rank
1 K 3
2 L 95
3 C 6
4 G 2
5 F 8
6 A 3
7 Y 12
8 O 1
9 B 18
10 M 35
... (many more)

53. Ranking
Rank Subsystem Actual Rank
1 K 3
2 L 95
3 C 6
4 G 2
5 F 8
6 A 3
7 Y 12
8 O 1
9 B 18
10 M 35
... (many more)

54. Ranking
Rank Subsystem Actual Rank
1 K 3
2 L 95
3 C 6
4 G 2
5 F 8
6 A 3
7 Y 12
8 O 1
9 B 18
10 M 35
... (many more)
Spearman correlation

55. Random splits

56. Random splits
4×50×

57. Random splits
4×50×

58. Linear regression

59. Linear regression

60. Linear regression
A higher predicted rank corresponds
to a higher observed rank

61. Impact of granularity

62. Impact of granularity
The predictions are more reliable
for coarse granularities…

63. Impact of granularity
The predictions are more reliable
for coarse granularities…
…at the cost of locality and stability.

64. Future work

65. Future work
• Assemble the pieces of the puzzle
• Evolution of dependencies predictors?
Are churned dependencies better
• Development process development?
What’s the impact of, say, global
• Human and social factors

66. Conclusion
• Cycles and cliques correlate with defects.
• The complexity of the dependency
structure predicts the number of defects.
• Defect predictions help to allocate
resources for QA more effectively.
Slides on Slideshare.net (search for ISSRE)

67. Contact
Email:
tz@acm.org
nachin@microsoft.com
Internet:
www.softevo.org
research.microsoft.com/esm