survey on software defect prediction (phd qualifying examination presentation)
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Survey on Software Defect Prediction (PhD Qualifying Examination Presentation)TRANSCRIPT
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Survey on Software Defect Prediction
- PhD Qualifying Examination -
July 3, 2014 Jaechang Nam
Department of Computer Science and Engineering HKUST
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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
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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)
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Ground Assumption
• The more complex, the more defect-prone
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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
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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)
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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
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Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s LOC
Simple Model
Met
rics
M
odel
s O
ther
s
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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
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Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s LOC
Simple Model
Fitting Model
Cyclomatic Metric
Halstead Metrics
Met
rics
M
odel
s O
ther
s
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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
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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).
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Defect Prediction Approaches
1970s 1980s 1990s 2000s 2010s LOC
Simple Model
Fitting Model Prediction Model (Regression)
Cyclomatic Metric
Halstead Metrics
Process Metrics
Met
rics
M
odel
s O
ther
s
Prediction Model (Classification)
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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
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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 misclassi f ication out of 78 modules) – Precision: 85% – Recal l : 73% – F-measure: 88%
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?"
Defect Prediction Process (Based on Machine Learning)
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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
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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
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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
Met
rics
M
odel
s O
ther
s
Just-In-Time Prediction Model
Practical Model and Applications
History Metrics
CK Metrics
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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
Met
rics
M
odel
s O
ther
s
History Metrics
CK Metrics
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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
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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)
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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
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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)
• Stil l l imited for individual developers in development phase
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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
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Change Classification (Kim@TSE`08) • Limitations
– Heavy model (11,500 features) – Not validated on commercial software products.
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Follow-up Studies • Studies addressing limitations
– “Reducing Features to Improve Code Change-Based Bug Prediction” (Shiva j i@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 classif ication • 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
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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
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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
Metrics
Models
Others
Fine-grained Prediction
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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
Met
rics
M
odel
s O
ther
s
History Metrics
CK Metrics
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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
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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
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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
Metrics
Models
Others
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Evaluation Measure for Practical Model
• Measure prediction performance based on code review effort
• AUCEC (Area Under Cost Effectiveness Curve)
33 Percent of LOC
Perc
ent
of b
ugs
foun
d
0 100%
100%
50% 10%
M1
M2
Rahman@FSE`11, Bugcache for inspections: Hit or miss?
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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)
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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
Met
rics
M
odel
s O
ther
s
Practical Model and Applications
Just-In-Time Prediction Model
History Metrics
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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”)
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• CK metrics (Chidamber&Kemerer@TSE`94)
• Prediction Performance of CK vs. code (Basili@TSE`96) – F-measure: 70% vs. 60%
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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
Met
rics
M
odel
s O
ther
s
Practical Model and Applications
Just-In-Time Prediction Model
History Metrics
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Representative History Metrics
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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
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Representative History Metrics • Advantage
– Better prediction performance than code metrics
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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
(%)
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History Metrics
• Limitations – History metrics do not extract par ticular program characteristics
such as developer social network, component network, and anti-pattern.
– Not applicable for new projects and projects lacking in historical data
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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
Met
rics
M
odel
s O
ther
s
History Metrics
Other Metrics
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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
Met
rics
M
odel
s O
ther
s
History Metrics
Other Metrics
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Other Metrics
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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
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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
Met
rics
M
odel
s O
ther
s
History Metrics
Other Metrics
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Defect Prediction for New Software Projects
• Universal Defect Prediction Model
• Cross-Project Defect Prediction
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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
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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
Met
rics
M
odel
s O
ther
s
History Metrics
Other Metrics
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Cross-Project Defect Prediction (CPDP)
• For a new project or a project lacking in the historical data
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?"
?"
?"
Training
Test
Model
Project A Project B
Only 2% out of 622 prediction combinations worked. (Zimmermann@FSE`09)
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Transfer Learning (TL)
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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
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CPDP
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• 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
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Metric Compensation (Watanabe@PROMISE`08)
• Key idea • New target metric value =
target metric value * average source metric value
average target metric value
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s
Source Target New Target
Let me transform like source!
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Metric Compensation (cont.) (Watanabe@PROMISE`08)
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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
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NN filter (Turhan@ESEJ`09)
• Key idea
• Nearest neighbor filter – Select 10 nearest source instances of each
target instance
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New Source Target
Hey, you look like me! Could you be my model?
Source
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NN filter (cont.) (Turhan@ESEJ`09)
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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
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Transfer Naive Bayes (Ma@IST`12)
• Key idea
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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
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Transfer Naive Bayes (cont.) (Ma@IST`12)
• Transfer Naive Bayes
– New prior probability
– New conditional probability
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Transfer Naive Bayes (cont.) (Ma@IST`12)
• How to find similar source instances for target – A similarity score
– A weight value
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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
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Transfer Naive Bayes (cont.) (Ma@IST`12)
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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
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TCA+ (Nam@ICSE`13)
• Key idea – TCA (Transfer Component Analysis)
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Source Target
Oops, we are different! Let’s meet in another world!
New Source New Target
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Transfer Component Analysis (cont.)
• Feature extraction approach – Dimensionality reduction – Projection • Map original data
in a lower-dimensional feature space
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Transfer Component Analysis (cont.)
• Feature extraction approach – Dimensionality reduction – Projection • Map original data
in a lower-dimensional feature space
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1-dimensional feature space
2-dimensional feature space
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Transfer Component Analysis (cont.)
• Feature extraction approach – Dimensionality reduction – Projection • Map original data
in a lower-dimensional feature space
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1-dimensional feature space
2-dimensional feature space
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TCA (cont.)
63 Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
Target domain data Source domain data
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TCA (cont.)
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TCA
Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
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TCA+ (Nam@ICSE`13)
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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+
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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
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Preliminary Results using TCA
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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-m
easu
re
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Preliminary Results using TCA
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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-m
easu
re
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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
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TCA+: #1. Characterize a Dataset
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3#
1#
…#
Dataset A Dataset B
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6#
11#
d1,2
d1,5
d1,3
d3,11
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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
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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
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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)
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TCA+ (cont.) (Nam@ICSE`13)
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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
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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
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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
Met
rics
M
odel
s O
ther
s
History Metrics
Other Metrics
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Feasibility Evaluation for CPDP • Solution for negative transfer
– Decision tree using project characteristic metrics (Zimmermann@FSE`09)
• E.g. programming language , # developers, etc .
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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.
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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)
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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
Metrics
Models
Others
History Metrics
Other Metrics
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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
Met
rics
M
odel
s O
ther
s
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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
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Source Target
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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
Metrics
Models
Others
Cross-Domain Prediction
History Metrics
Other Metrics
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Other Topics
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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
Met
rics
M
odel
s O
ther
s
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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)
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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
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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
Met
rics
M
odel
s O
ther
s
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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
Metrics
Models
Others
Cross-Domain Prediction
Fine-grained Prediction
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Thank you!
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