studying the impact of dependency network measures on software quality
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TRANSCRIPT
Thanh H. D. Nguyen, Bram Adams, Ahmed E. Hassan SAIL, School of Compu?ng, Queen’s University, Kingston, Canada
Studying the impact of dependency network measures on soIware quality
Problem: Quality improvement resources are limited
Solu?on: Bug predic5on iden5fies defect-‐prone modules
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Code Quality
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Bug predic?on models
Bug Predic5on Model
High Recall -‐> We won’t miss a possible bug High Precision -‐> We won’t waste effort
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A
F B
C
G E
D
SoIware is more than just size and complexity
Node"
Local Neighborhood"
Global Neighborhood"
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SoIware is more than just size and complexity
Social Network Measures!
(SNA)"
Traditional Metrics (MET)"Node"
Local Neighborhood"
Global Neighborhood"
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Bug Predic5on Model
Would SNA improve performance?
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Would SNA improve performance?
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Would SNA improve performance?
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Would SNA improve performance?
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Would SNA improve performance?
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Why Eclipse?
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Bug Predic5on Model
Would SNA improve performance?
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Bug Predic5on Model
Would SNA improve performance?
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+25% for Recall and Precision
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Does this generalize?
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12 Metrics
11 Metrics
12 Metrics
Node"
Local Neighborhood"
Global Neighborhood"
Which metrics provide the improvement?
Use hierarchical modeling to find important group [Caltado et al. TSE10]
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12 Metrics
11 Metrics
12 Metrics
Node"
Local Neighborhood"
Global Neighborhood"
7%
+2.7%
+0.3%
Which metrics provide the improvement?
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12 Metrics
11 Metrics
12 Metrics
Node"
Local Neighborhood"
Global Neighborhood"
7%
+2.7%
+0.3%
Local neighbours have most of the important improvement
Which metrics provide the improvement?
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Which local measures have the most impact?
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Cluster fan-‐in
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Cluster fan-‐in
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Layer bypass
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Layer bypass
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Layer bypass
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Consider your neighbor connec?ons
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How well do we perform in prac?ce?
✔ ✗
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Effort Aware Predic?on Models
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Comparing Performance Using Effort Aware Curves
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
htFile A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Risk 0.78 0.56 0.34
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Comparing Performance Using Effort Aware Curves
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
ht
A
File A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Risk 0.78 0.56 0.34
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Comparing Performance Using Effort Aware Curves
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
htB
File A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Risk 0.78 0.56 0.34
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Comparing Performance Using Effort Aware Curves
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
ht
C
File A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Risk 0.78 0.56 0.34
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Is this a good predic?on?
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
htFile A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Risk 0.78 0.56 0.34
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Beeer predic?on means a higher curve
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
htFile A B C
#bug 0 1 2
LOC 48 8 44
ROI 0 0.125 0.045
Bad 0.78 0.56 0.34
Good 0.32 0.72 0.55
Good
Bad
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The predic?on model helps reduce tes?ng effort
0 20 40 60 80 100
020
4060
8010
0
% lines of code reviewed
% b
ugs
caug
ht
File Package
Random File
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37 Thanh H. D. Nguyen ([email protected])
Deviance explained
Bugginess ~ Traditional metrics + Local + Global
+2.7%"
+1.1%"
+0.3%"
+1,9%"
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Anova on M3