Survey on Software Defect Prediction (PhD Qualifying Examination Presentation)

1. Survey on
Software Defect Prediction
- PhD Qualifying Examination -
July 3, 2014
Jaechang Nam
Department of Computer Science and Engineering
HKUST

2. Outline
• Background
• Software Defect Prediction Approaches
– Simple metric and defect estimation models
– Complexity metrics and Fitting models
– Prediction models
– Just-In-Time Prediction Models
– Practical Prediction Models and Applications
– History Metrics from Software Repositories
– Cross-Project Defect Prediction and Feasibility
• Summary and Challenging Issues
2

3. Motivation
• General question of software defect prediction
– Can we identify defect-prone entities (source code
file, binary, module, change,...) in advance?
• # of defects
• buggy or clean
• Why?
– Quality assurance for large software
(Akiyama@IFIP’71)
– Effective resource allocation
• Testing (Menzies@TSE`07)
• Code review (Rahman@FSE’11)
3

4. Ground Assumption
• The more complex, the more defect-prone
4

5. Two Focuses on Defect Prediction
• How much complex are software and its
process?
– Metrics
• How can we predict whether software has
defects?
– Models based on the metrics
5

6. Prediction Performance Goal
• Recall vs. Precision
• Strong predictor criteria
– 70% recall and 25% false positive rate (Menzies@TSE`07)
– Precision, recall, accuracy ≥ 75% (Zimmermann@FSE`09)
6

7. Outline
• Background
• Software Defect Prediction Approaches
– Simple metric and defect estimation models
– Complexity metrics and Fitting models
– Prediction models
– Just-In-Time Prediction Models
– Practical Prediction Models and Applications
– History Metrics from Software Repositories
– Cross-Project Defect Prediction and Feasibility
• Summary and Challenging Issues
7

8. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
MetricsModelsOthers

9. Identifying Defect-prone Entities
• Akiyama’s equation (Ajiyama@IFIP`71)
– # of defects = 4.86 + 0.018 * LOC (=Lines Of Code)
• 23 defects in 1 KLOC
• Derived from actual systems
• Limitation
– Only LOC is not enough to capture software
complexity
9

10. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Cyclomatic
Metric
Halstead
Metrics
MetricsModelsOthers

11. Complexity Metrics and Fitting Models
• Cyclomatic complexity metrics (McCabe`76)
– “Logical complexity” of a program represented in
control flow graph
– V(G) = #edge – #node + 2
• Halstead complexity metrics (Halsted`77)
– Metrics based on # of operators and operands
– Volume = N * log2n
– # of defects = Volume / 3000
11

12. Complexity Metrics and Fitting Models
• Limitation
– Do not capture complexity (amount) of change.
– Just fitting models but not prediction models in most of
studies conducted in 1970s and early 1980s
• Correlation analysis between metrics and # of defects
– By linear regression models
• Models were not validated for new entities (modules).
12

13. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Cyclomatic
Metric
Halstead
Metrics
Process
Metrics
MetricsModelsOthers
Prediction Model (Classification)

14. Regression Model
• Shen et al.’s empirical study (Shen@TSE`85)
– Linear regression model
– Validated on actual new modules
– Metrics
• Halstead, # of conditional statements
• Process metrics
– Delta of complexity metrics between two successive system versions
– Measures
• Between actual and predicted # of defects on new modules
– MRE (Mean magnitude of relative error)
» average of (D-D’)/D for all modules
• D:#actual###of#defects#
• D’:#predicted###of#defects##
» MRE = 0.48
14

15. Classification Model
• Discriminative analysis by Munson et al. (Munson@TSE`92)
• Logistic regression
• High risk vs. low risk modules
• Metrics
– Halstead and Cyclomatic complexity metrics
• Measure
– Type I error: False positive rate
– Type II error: False negative rate
• Result
– Accuracy: 92% (6 misclassification out of 78 modules)
– Precision: 85%
– Recall: 73%
– F-measure: 88%
15

16. ?"
Defect Prediction Process
(Based on Machine Learning)
16
Classification /
Regression
Software
Archives
B"
C"
C"
B"
...
2"
5"
0"
1"
...
Instances with
metrics (features)
and labels
B"
C"
B"
...
2"
0"
1"
...
Training Instances(Preprocessing)
Model
?"
New instances
Generate
Instances
Build
a model

17. Defect Prediction
(Based on Machine Learning)
• Limitations
– Limited resources for process metrics
• Error fix in unit testing phase was conducted informally by
an individual developer (no error information available in
this phase). (Shen@TSE`85)
– Existing metrics were not enough to capture
complexity of object-oriented (OO) programs.
– Helpful for quality assurance team but not for
individual developers
17

18. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
Process
Metrics
MetricsModelsOthers
Just-In-Time Prediction Model
Practical Model and Applications
History
Metrics
CK Metrics

19. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
Just-In-Time Prediction Model
Practical Model and Applications
Process
Metrics
MetricsModelsOthers
History
Metrics
CK Metrics

20. Risk Prediction of Software Changes
(Mockus@BLTJ`00)
• Logistic regression
• Change metrics
– LOC added/deleted/modified
– Diffusion of change
– Developer experience
• Result
– Both false positive and false negative rate: 20% in the
best case
20

21. Risk Prediction of Software Changes
(Mockus@BLTJ`00)
• Advantage
– Show the feasible model in practice
• Limitation
– Conducted 3 times per week
• Not fully Just-In-Time
– Validated on one commercial system (5ESS switching
system software)
21

22. BugCache (Kim@ICSE`07)
• Maintain defect-prone entities in a cache
• Approach
• Result
– Top 10% files account for 73-95% of defects on 7 systems
22

23. BugCache (Kim@ICSE`07)
• Advantages
– Cache can be updated quickly with less cost. (c.f. static
models based on machine learning)
– Just-In-Time: always available whenever QA teams want to get
the list of defect-prone entities
• Limitations
– Cache is not reusable for other software projects.
– Designed for QA teams
• Applicable only in a certain time point after a bunch of changes (e.g.,
end of a sprint)
• Still limited for individual developers in development phase
23

24. Change Classification (Kim@TSE`08)
• Classification model based on SVM
• About 11,500 features
– Change metadata such as changed LOC, change count
– Complexity metrics
– Text features from change log messages, source code, and file
names
• Results
– 78% accuracy and 60% recall on average from 12 open-source
projects
24

25. Change Classification (Kim@TSE`08)
• Limitations
– Heavy model (11,500 features)
– Not validated on commercial software products.
25

26. Follow-up Studies
• Studies addressing limitations
– “Reducing Features to Improve Code Change-Based Bug
Prediction” (Shivaji@TSE`13)
• With less than 10% of all features, buggy F-measure is 21% improved.
– “Software Change Classification using Hunk
Metrics” (Ferzund@ICSM`09)
• 27 hunk-level metrics for change classification
• 81% accuracy, 77% buggy hunk precision, and 67% buggy hunk recall
– “A large-scale empirical study of just-in-time quality
assurance” (Kamei@TSE`13)
• 14 process metrics (mostly from Mockus`00)
• 68% accuracy, 64% recall on 11open-source and commercial projects
– “An Empirical Study of Just-In-Time Defect Prediction Using
Cross-Project Models” (Fukushima@MSR`14)
• Median AUC: 0.72
26

27. Challenges of JIT model
• Practical validation is difficult
– Just 10-fold cross validation in current literature
– No validation on real scenario
• e.g., online machine learning
• Still difficult to review huge change
– Fine-grained prediction within a change
• e.g., Line-level prediction
27

28. Next Steps of Defect Prediction
1980s 1990s 2000s 2010s 2020s
Online Learning JIT Model
Prediction Model (Regression)
Prediction Model (Classification)
Just-In-Time Prediction Model
Process
Metrics
MetricsModelsOthers
Fine-grained
Prediction

29. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
Just-In-Time Prediction Model
Practical Model and Applications
Process
Metrics
MetricsModelsOthers
History
Metrics
CK Metrics

30. Defect Prediction in Industry
• “Predicting the location and number of faults in large
software systems” (Ostrand@TSE`05)
– Two industrial systems
– Recall 86%
– 20% most fault-prone modules account for 62% faults
30

31. Case Study for Practical Model
• “Does Bug Prediction Support Human Developers?
Findings From a Google Case Study” (Lewis@ICSE`13)
– No identifiable change in developer behaviors after using defect
prediction model
• Required characteristics but very challenging
– Actionable messages / obvious reasoning
31

32. Next Steps of Defect Prediction
1980s 1990s 2000s 2010s 2020s
Actionable
Defect
Prediction
Prediction Model (Regression)
Prediction Model (Classification)
Just-In-Time Prediction Model
Practical Model and Applications
Process
Metrics
MetricsModelsOthers

33. Evaluation Measure for Practical Model
• Measure prediction performance based on code
review effort
• AUCEC (Area Under Cost Effectiveness Curve)
33
Percent of LOC
Percentofbugsfound
0
100%
100%
50%10%
M1
M2
Rahman@FSE`11, Bugcache for inspections: Hit or miss?

34. Practical Application
• What else can we do more with defect
prediction models?
– Test case selection on regression testing
(Engstrom@ICST`10)
– Prioritizing warnings from FindBugs (Rahman@ICSE`14)
34

35. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Process
Metrics
MetricsModelsOthers
Practical Model and Applications
Just-In-Time Prediction Model
History
Metrics

36. Representative OO Metrics
Metric Description
WMC Weighted Methods per Class (# of methods)
DIT Depth of Inheritance Tree ( # of ancestor classes)
NOC Number of Children
CBO Coupling between Objects (# of coupled classes)
RFC Response for a class: WMC + # of methods called by the class)
LCOM Lack of Cohesion in Methods (# of "connected components”)
36
• CK metrics (Chidamber&Kemerer@TSE`94)
• Prediction Performance of CK vs. code
(Basili@TSE`96)
– F-measure: 70% vs. 60%

37. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Process
Metrics
MetricsModelsOthers
Practical Model and Applications
Just-In-Time Prediction Model
History
Metrics

38. Representative History Metrics
38
Name
# of
metrics
Metric
source
Citation
Relative code change churn 8 SW Repo.* Nagappan@ICSE`05
Change 17 SW Repo. Moser@ICSE`08
Change Entropy 1 SW Repo. Hassan@ICSE`09
Code metric churn
Code Entropy
2 SW Repo. D’Ambros@MSR`10
Popularity 5 Email archive Bacchelli@FASE`10
Ownership 4 SW Repo. Bird@FSE`11
Micro Interaction Metrics (MIM) 56 Mylyn Lee@FSE`11
* SW Repo. = version control system + issue tracking system

39. Representative History Metrics
• Advantage
– Better prediction performance than code metrics
39
0.0%#
10.0%#
20.0%#
30.0%#
40.0%#
50.0%#
60.0%#
Moser`08# Hassan`09# D'Ambros`10# Bachille`10# Bird`11# Lee`11#
Performance Improvement
(all metrics vs. code complexity metrics)
(F-measure) (F-measure)(Absolute
prediction
error)
(Spearman
correlation)
(Spearman
correlation)
(Spearman
correlation*)
(*Bird`10’s results are from two metrics vs. code metrics, No comparison data in Nagappan`05)
Performance
Improvement
(%)

40. History Metrics
• Limitations
– History metrics do not extract particular program characteristics
such as developer social network, component network, and
anti-pattern.
– Not applicable for new projects and projects lacking in
historical data
40

41. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

42. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

43. Other Metrics
43
Name
# of
metrics
Metric
source
Citation
Component network 28
Binaries
(Windows
Server 2003)
Zimmermann@ICSE`08
Developer-Module network 9
SW Repo. +
Binaries
Pinzger@FSE`08
Developer social network 4 SW Repo. Meenely@FSE`08
Anti-pattern 4
SW Repo. +
Design-
pattern
Taba@ICSM`13
* SW Repo. = version control system + issue tracking system

44. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

45. Defect Prediction for New Software Projects
• Universal Defect Prediction Model
• Cross-Project Defect Prediction
45

46. Universal Defect Prediction Model
(Zhang@MSR`14)
• Context-aware rank transformation
– Transform metric values ranged from 1 to 10 across all
projects.
• Model built by 1398 projects collected from
SourceForge and Google code
46

47. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

48. Cross-Project Defect Prediction
(CPDP)
• For a new project or a project lacking in
the historical data
48
?"
?"
?"
Training
Test
Model
Project A Project B
Only 2% out of 622 prediction combinations worked. (Zimmermann@FSE`09)

49. Transfer Learning (TL)
27
Traditional Machine Learning (ML)
Learning
System
Learning
System
Transfer Learning
Learning
System
Learning
System
Knowledge
Transfer
Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis

50. CPDP
50
• Adopting transfer learning
Transfer learning
Metric
Compensation
NN Filter TNB TCA+
Preprocessing N/A
Feature selection,
Log-filter
Log-filter Normalization
Machine learner C4.5 Naive Bayes TNB Logistic Regression
# of Subjects 2 10 10 8
# of predictions 2 10 10 26
Avg. f-measure
0.67
(W:0.79, C:0.58)
0.35
(W:0.37, C:0.26)
0.39
(NN: 0.35, C:0.33)
0.46
(W:0.46, C:0.36)
Citation Watanabe@PROMISE`08 Turhan@ESEJ`09 Ma@IST`12 Nam@ICSE`13
* NN = Nearest neighbor,W = Within, C = Cross

51. Metric Compensation
(Watanabe@PROMISE`08)
• Key idea
• New target metric value =
target metric value * average source metric value
average target metric value
51
Source Target New Target
Let me transform like source!

52. Metric Compensation (cont.)
(Watanabe@PROMISE`08)
52
Transfer learning
Metric
Compensation
NN Filter TNB TCA+
Preprocessing N/A
Feature selection,
Log-filter
Log-filter Normalization
Machine learner C4.5 Naive Bayes TNB Logistic Regression
# of Subjects 2 10 10 8
# of predictions 2 10 10 26
Avg. f-measure
0.67
(W:0.79, C:0.58)
0.35
(W:0.37, C:0.26)
0.39
(NN: 0.35, C:0.33)
0.46
(W:0.46, C:0.36)
Citation Watanabe@PROMISE`08 Turhan@ESEJ`09 Ma@IST`12 Nam@ICSE`13
* NN = Nearest neighbor,W = Within, C = Cross

53. NN filter
(Turhan@ESEJ`09)
• Key idea
• Nearest neighbor filter
– Select 10 nearest source instances of each
target instance
53
New Source Target
Hey, you look like me! Could you be my model?
Source

54. NN filter (cont.)
(Turhan@ESEJ`09)
54
Transfer learning
Metric
Compensation
NN Filter TNB TCA+
Preprocessing N/A
Feature selection,
Log-filter
Log-filter Normalization
Machine learner C4.5 Naive Bayes TNB Logistic Regression
# of Subjects 2 10 10 8
# of predictions 2 10 10 26
Avg. f-measure
0.67
(W:0.79, C:0.58)
0.35
(W:0.37, C:0.26)
0.39
(NN: 0.35, C:0.33)
0.46
(W:0.46, C:0.36)
Citation Watanabe@PROMISE`08 Turhan@ESEJ`09 Ma@IST`12 Nam@ICSE`13
* NN = Nearest neighbor,W = Within, C = Cross

55. Transfer Naive Bayes
(Ma@IST`12)
• Key idea
55
Target
Hey, you look like me!You will get more chance to be my best model!
Source
! Provide more weight to similar source instances to build a Naive Bayes Model
Build a model
Please, consider me more important than other instances

56. Transfer Naive Bayes (cont.)
(Ma@IST`12)
• Transfer Naive Bayes
– New prior probability
– New conditional probability
56

57. Transfer Naive Bayes (cont.)
(Ma@IST`12)
• How to find similar source instances for target
– A similarity score
– A weight value
57
F1 F2 F3 F4 Score (si)
Max of target 7 3 2 5 -
src. inst 1 5 4 2 2 3
src. inst 2 0 2 5 9 1
Min of target 1 2 0 1 -
k=# of features, si=score of instance i

58. Transfer Naive Bayes (cont.)
(Ma@IST`12)
58
Transfer learning
Metric
Compensation
NN Filter TNB TCA+
Preprocessing N/A
Feature selection,
Log-filter
Log-filter Normalization
Machine learner C4.5 Naive Bayes TNB Logistic Regression
# of Subjects 2 10 10 8
# of predictions 2 10 10 26
Avg. f-measure
0.67
(W:0.79, C:0.58)
0.35
(W:0.37, C:0.26)
0.39
(NN: 0.35, C:0.33)
0.46
(W:0.46, C:0.36)
Citation Watanabe@PROMISE`08 Turhan@ESEJ`09 Ma@IST`12 Nam@ICSE`13
* NN = Nearest neighbor,W = Within, C = Cross

59. TCA+
(Nam@ICSE`13)
• Key idea
– TCA (Transfer Component Analysis)
59
Source Target
Oops, we are different! Let’s meet in another world!
New Source New Target

60. Transfer Component Analysis (cont.)
• Feature extraction approach
– Dimensionality reduction
– Projection
• Map original data
in a lower-dimensional feature space
60

61. Transfer Component Analysis (cont.)
• Feature extraction approach
– Dimensionality reduction
– Projection
• Map original data
in a lower-dimensional feature space
61
1-dimensional feature space
2-dimensional feature space

62. Transfer Component Analysis (cont.)
• Feature extraction approach
– Dimensionality reduction
– Projection
• Map original data
in a lower-dimensional feature space
62
1-dimensional feature space
2-dimensional feature space

63. TCA (cont.)
63
Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
Target domain data
Source domain data

64. TCA (cont.)
64
TCA
Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis

65. TCA+
(Nam@ICSE`13)
65
Source Target
Oops, we are different! Let’s meet at another world!
New Source New Target
But, we are still a bit different!
Source Target
Oops, we are different! Let’s meet at another world!
New Source New Target
Normalize US together!
TCA
TCA+

66. Normalization Options
• NoN: No normalization applied
• N1: Min-max normalization (max=1, min=0)
• N2: Z-score normalization (mean=0, std=1)
• N3: Z-score normalization only using source mean and
standard deviation
• N4: Z-score normalization only using target mean and
standard deviation
13

67. Preliminary Results using TCA
0#
0.1#
0.2#
0.3#
0.4#
0.5#
0.6#
0.7#
0.8#
F*measure"
67*Baseline:#CrossLproject#defect#predicNon#without#TCA#and#normalizaNon#
Baseline NoN N1 N2 N3 N4 Baseline NoN N1 N2 N3 N4
Project A ! Project B Project B ! Project A
F-measure

68. Preliminary Results using TCA
0#
0.1#
0.2#
0.3#
0.4#
0.5#
0.6#
0.7#
0.8#
F*measure"
68*Baseline:#CrossLproject#defect#predicNon#without#TCA#and#normalizaNon#
Prediction performance of TCA
varies according to different
normalization options!
Baseline NoN N1 N2 N3 N4 Baseline NoN N1 N2 N3 N4
Project A ! Project B Project B ! Project A
F-measure

69. TCA+: Decision Rules
• Find a suitable normalization for TCA
• Steps
– #1: Characterize a dataset
– #2: Measure similarity
between source and target datasets
– #3: Decision rules
69

70. TCA+: #1. Characterize a Dataset
70
3#
1#
…#
Dataset A Dataset B
2#
4#
5#
8#
9#
6#
11#
d1,2
d1,5
d1,3
d3,11
3#
1#
…#
2#
4#
5#
8#
9#
6#
11#
d2,6
d1,2
d1,3
d3,11
DIST={dij : i,j, 1 ≤ i < n, 1 < j ≤ n, i < j}
A

71. TCA+: #2. Measure Similarity
between Source and Target
• Minimum (min) and maximum (max) values of DIST
• Mean and standard deviation (std) of DIST
• The number of instances
71

72. TCA+: #3. Decision Rules
• Rule #1
– Mean and Std are same ! NoN
• Rule #2
– Max and Min are different ! N1 (max=1, min=0)
• Rule #3,#4
– Std and # of instances are different
! N3 or N4 (src/tgt mean=0, std=1)
• Rule #5
– Default ! N2 (mean=0, std=1)
72

73. TCA+ (cont.)
(Nam@ICSE`13)
73
Transfer learning
Metric
Compensation
NN Filter TNB TCA+
Preprocessing N/A
Feature selection,
Log-filter
Log-filter Normalization
Machine learner C4.5 Naive Bayes TNB Logistic Regression
# of Subjects 2 10 10 8
# of predictions 2 10 10 26
Avg. f-measure
0.67
(W:0.79, C:0.58)
0.35
(W:0.37, C:0.26)
0.39
(NN: 0.35, C:0.33)
0.46
(W:0.46, C:0.36)
Citation Watanabe@PROMISE`08 Turhan@ESEJ`09 Ma@IST`12 Nam@ICSE`13
* NN = Nearest neighbor,W = Within, C = Cross

74. Current CPDP using TL
• Advantages
– Comparable prediction performance to within-prediction
models
– Benefit from the state-of-the-art TL approaches
• Limitation
– Performance of some cross-prediction pairs is still poor.
(Negative Transfer)
74
Source Target

75. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

76. Feasibility Evaluation for CPDP
• Solution for negative transfer
– Decision tree using project characteristic metrics (Zimmermann@FSE`09)
• E.g. programming language, # developers, etc.
76

77. Follow-up Studies
• “An investigation on the feasibility of cross-project
defect prediction.” (He@ASEJ`12)
– Decision tree using distributional characteristics of a dataset
E.g. mean, skewness, peakedness, etc.
77

78. Feasibility for CPDP
• Challenges on current studies
– Decision trees were not evaluated properly.
• Just fitting model
– Low target prediction coverage
• 5 out of 34 target projects were feasible for cross-predictions
(He@ASEJ`12)
78

79. Next Steps of Defect Prediction
1980s 1990s 2000s 2010s 2020s
Cross-Prediction
Feasibility Model
Prediction Model (Regression)
Prediction Model (Classification)
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
History
Metrics
Other Metrics

80. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
History
Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Other Metrics
Practical Model and Applications
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers

81. Cross-prediction Model
• Common challenge
– Current cross-prediction models are limited to datasets with
same number of metrics
– Not applicable on projects with different feature spaces
(different domains)
• NASA Dataset: Halstead, LOC
• Apache Dataset: LOC, Cyclomatic, CK metrics
81
Source Target

82. Next Steps of Defect Prediction
1980s 1990s 2000s 2010s 2020s
Prediction Model (Regression)
Prediction Model (Classification)
CK Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Practical Model and Applications
Data Privacy
Personalized
Model
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
Cross-Domain
Prediction
History Metrics
Other Metrics

83. Other Topics
83

84. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
History Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Other Metrics
Practical Model and Applications
Data Privacy
Personalized
Model
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers

85. Other Topics
• Privacy issue on defect datasets
– MORPH (Peters@ICSE`12)
• Mutate defect datasets while keeping prediction accuracy
• Can accelerate cross-project defect prediction with industrial
datasets
• Personalized defect prediction model (Jiang@ASE`13)
– “Different developers have different coding styles, commit
frequencies, and experience levels, all of which cause different
defect patterns.”
– Results
• Average F-measure: 0.62 (personalized models) vs. 0.59 (non-
personalized models)
85

86. Outline
• Background
• Software Defect Prediction Approaches
– Simple metric and defect estimation models
– Complexity metrics and Fitting models
– Prediction models
– Just-In-Time Prediction Models
– Practical Prediction Models and Applications
– History Metrics from Software Repositories
– Cross-Project Defect Prediction and Feasibility
• Summary and Challenging Issues
86

87. Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s
LOC
Simple Model
Fitting Model
Prediction Model (Regression)
Prediction Model (Classification)
Cyclomatic
Metric
Halstead
Metrics
CK Metrics
History Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Other Metrics
Practical Model and Applications
Data Privacy
Personalized
Model
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers

88. Next Steps of Defect Prediction
1980s 1990s 2000s 2010s 2020s
Online Learning JIT Model
Actionable
Defect
Prediction
Cross-Prediction
Feasibility Model
Prediction Model (Regression)
Prediction Model (Classification)
CK Metrics
History Metrics
Just-In-Time Prediction Model
Cross-Project Prediction
Other Metrics
Practical Model and Applications
Data Privacy
Personalized
Model
Universal
Model
Process
Metrics
Cross-Project
Feasibility
MetricsModelsOthers
Cross-Domain
Prediction
Fine-grained
Prediction

89. Thank you!
89