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Feasibility evaluation on cross-project defect prediction

Akiyama’s model is the earliest prediction model that predicts the number defects by using size of software such as LOC, # of subroutine calls. IFIC=International Federation of Information Processing

Testing a entire system is not feasible. (Menzies`07)

Inspecting source code is costly as well. (Rahman`11)

Feasibility evaluation on cross-project defect prediction

n = total # of distinct operands and operators

N = total # of distinct operands and operators

Correlation analysis using linear regression

n = total # of distinct operands and operators

N = total # of distinct operands and operators

Correlation analysis using linear regression

Considered different thresholds for discriminative probability

Developer experience: # of previous changes by the same developer. Weighted by considering contributions of the set of developers.

Developer experience: # of previous changes by the same developer. Weighted by considering contributions of the set of developers.

Then, cache is updated with source code files based on locality.

When the cache is full, the cache is replaced based on Least recently used policy that is used for common cache policy in operation system

Based on cache hit or miss, update the cache

Cache miss: entities fixed are not in cache

Load the entities and nearby entities (locality) to the cache

Locality

Files/functions changed together with defects

Recently added files/functions

Recently changed files/functions

Cache replacement policy:

Least Recently Used (LRU) weighted by # of previous defects.

Fukushima: cross-prediction performance can be improved

DIT: # of ancestor classes

NOC: the number of direct descendants (subclasses) for each class

CBO:

RFC:

LCOM: the number of "connected components" in a class

Cohesion – 연관있는 메서드들은 한 클래안에 다 모아 넣기 high cohesion

Coupling –

Change: e.g., # of revisions, # of authors editing a file

Change Entropy: quantify complexity of changes by using Entropy theory. How many times changed in the same period.

Code metric churn: churned metric value, collected biweekly basis.

Code Entropy: how many lines of code changed in the same period?

Popularity: source code files discussed a lot in emails

Ownership: % of commits by a developer for a source code file

MIM: How long does the source code file is edited.

20% decrease of AIC, 200% increase of D^2 (Taba`10)

Average Within F-measure: 0.42

Average Universal F-measure: 0.39

Average Within AUC: 0.72

Average Universal AUC: 0.72

This is same as within-project defect prediction in this research.

However, transfer learning is reusing knowledge from a certain domain which has enough data.

Cross prediction is simply reusing learning system of a source project for a target project.However, in transfer learning, we just extract proper knowledge, which will be really helpful for the target domain.

So, transfer learning algorithms play a role for smart knowledge transfer for the target domain.

This is more than just a simple cross prediction.

How to transfer knowledge from a source is a transfer learning algorithm!

mass of test data = kmM

kmM^2은 constant

k = # of feautres

s_i = similarity score of instance i

m = # of instances

M = mass of one feature in one instance

source instance: s_i * M

m * k * M

mass of a source instance = sM

mass of test data = kmM

kmM^2은 constant

TNB didn’t report within-results

Feature extraction is achieved by a technique called projection. Projection technique maps original data in a low dimensional feature space.

Here is an example of 2-dimensional feature space of a data set. There are four instances labeled.

We project a light on this space to 1-dimensional space, and then four instances are mapped in the one-dimensional space.

PCA is just for reducing feature space dimensionality. However, Transfer component analysis, TCA, try to find a new feature space where the distribution of source and target data sets are similar by projection.

The representative technique is PCA.

I’d like to show how PCA is different from TCA by an example.

In two-dimensional space, there are source and target data sets and we can see distributions are clearly different.

If we apply PCA and TCA , and then we can get the following results in one-dimensional space.

Probability mass function

In PCA, instances are projected into one dimensional space, however, distribution between source and target are still different.

In TCA, all instances are also projected in one-dimensional space, where distribution between source and target is similar.

Positive and negative instance of both training and test domains have discriminative power as shown in this figure.

You can check detailed equations about this algorithm in this paper

[add labels]

NI is min-max normalization which makes maximum and minimum value as 1 and 0 respectively.

N2 is z-score normalization which makes mean and standard deviation as 0 and 1 respectively.

We assume that some data sets may not have enough statistical information. So we defined variations of z-score normalization.

To normalize both source and target data sets, N3 is only using mean and standard deviation from source data (when target data does not have enough statistical information. For example, lack of instances in a data set.

N4 is only using target information for normalizing both source and target data sets.

Baseline means cross-project prediction without and normalization

In Safe to Apache, all TCA results with or without normalization are better than baseline.

However, in Apache to safe, N1, N3 didn’t outperform Baseline. This could be observed in other prediction combinations.

So, we could conclude prediction performance of TCA varies according to different normalization options.

For the decision rules, we first characterize both source and target data sets to identify their difference.

In the second step, we measure similarity between source and target data sets.

With degree of similarity, we created decision rules!

Here are two data sets.

Intuitively, Data set A’s distribution is more sparser than data set B.

To quantify this difference, we compute Euclidean distance of all pairs of instances in each data set.

We defined DIST set for distances of all pairs.

Likewise, we can get DIST set from Data set B.

With these information, we creted decision rules

If mean and std is same, we assume that distributions bewteen source and target is same. So we applied no normalization.

For Rule2, if max and min values are different, we used N1(min-max normalization)

for Rule3 and 4, we considered std and # of instances. If target information is not enough, then we used source mean and std to normalize both datasets.

In case of Rule 5, if there are no rules are applicable, we applied N2 option, which make mean and std as 0 and 1 respectively.

Precision > 0.5 and Recall > 0.7

Tested on 5 external projects

Average Within F-measure: 0.42

Average Universal F-measure: 0.39

Average Within AUC: 0.72

Average Universal F-measure: 0.72

Feasibility evaluation on cross-project defect prediction

DIT: # of ancestor classes

NOC: the number of direct descendants (subclasses) for each class

CBO:

RFC:

LCOM: the number of "connected components" in a class

Cohesion – 연관있는 메서드들은 한 클래안에 다 모아 넣기 high cohesion

Coupling –

- 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 is 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 Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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) Cyclomati c Metric Halstea d 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. – Noise data • Bias in Bug-Fix Dataset(Bird@FSE`09) – 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) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Noise Reduction Semi- supervised/active
- 42. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Noise Reduction Semi- supervised/active
- 43. Other Metrics 43 Name # of metrics Metric source Citation Component network 28 Binaries (Windows Server 2003) Zimmermann@ICSE`0 8 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) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Noise Reduction Semi- supervised/active
- 45. Noise Reduction • Noise detection and elimination algorithm (Kim@ICSE`11) – Closest List Noise Identification (CLNI) • Based on Euclidean distance between instances – Average F-measure improvement • 0.504 0.621 • Relink (Wo@FSE`11) – Recover missing links between bugs and changes – 60% 78% recall for missing links – F-measure improvement • e.g. 0.698 (traditional) 0.731 (ReLink) 45
- 46. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Semi- supervised/active
- 47. Defect Prediction for New Software Projects • Universal Defect Prediction Model • Simi-supervised / active learning • Cross-Project Defect Prediction 47
- 48. 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 48
- 49. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Semi- supervised/active
- 50. Other approaches for CDDP • Semi-supervised learning with dimension reduction for defect prediction (Lu@ASE`12) – Training a model by a small set of labeled instances together with many unlabeled instances – AUC improvement • 0.83 0.88 with 2% labeled instances • Sample-based semi-supervised/active learning for defect prediction (Li@AESEJ`12) – Average F-measure • 0.628 0.685 with 10% sampled instances 50
- 51. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Semi- supervised/active
- 52. Cross-Project Defect Prediction (CPDP) • For a new project or a project lacking in the historical data 52 ? ? ? Training Test Model Project A Project B Only 2% out of 622 prediction combinations worked. (Zimmermann@FSE`09)
- 53. Transfer Learning (TL) 27 Traditional Machine Learning (ML) Learnin g System Learnin g System Transfer Learning Learnin g System Learnin g System Knowledge Transfer Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
- 54. CPDP 54 • 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
- 55. Metric Compensation (Watanabe@PROMISE`08) • Key idea • New target metric value = target metric value * average source metric value average target metric value 55 s Source Target New Target
- 56. Metric Compensation (cont.) (Watanabe@PROMISE`08) 56 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
- 57. NN filter (Turhan@ESEJ`09) • Key idea • Nearest neighbor filter – Select 10 nearest source instances of each target instance 57 New Source Target Hey, you look like me! Could you be my model? Source
- 58. NN filter (cont.) (Turhan@ESEJ`09) 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. Transfer Naive Bayes (Ma@IST`12) • Key idea 59 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
- 60. Transfer Naive Bayes (cont.) (Ma@IST`12) • Transfer Naive Bayes – New prior probability – New conditional probability 60
- 61. Transfer Naive Bayes (cont.) (Ma@IST`12) • How to find similar source instances for target – A similarity score – A weight value 61 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
- 62. Transfer Naive Bayes (cont.) (Ma@IST`12) 62 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
- 63. TCA+ (Nam@ICSE`13) • Key idea – TCA (Transfer Component Analysis) 63 Source Target Oops, we are different! Let’s meet in another world! New Source New Target
- 64. Transfer Component Analysis (cont.) • Feature extraction approach – Dimensionality reduction – Projection • Map original data in a lower-dimensional feature space 64 1-dimensional feature space 2-dimensional feature space
- 65. TCA (cont.) 65 Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis Target domain data Source domain data
- 66. TCA (cont.) 66 TCA Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
- 67. TCA+ (Nam@ICSE`13) 67 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+
- 68. 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
- 69. Preliminary Results using TCA 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 F-measure 69*Baseline: Cross-project defect prediction without TCA and normalization 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
- 70. 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 70
- 71. TCA+: #1. Characterize a Dataset 71 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
- 72. 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 72
- 73. 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) 73
- 74. TCA+ (cont.) (Nam@ICSE`13) 74 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
- 75. 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) 75 Source Target
- 76. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics Just-In-Time Prediction Model Cross-Project Prediction Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Semi- supervised/active
- 77. Feasibility Evaluation for CPDP • Solution for negative transfer – Decision tree using project characteristic metrics (Zimmermann@FSE`09) • E.g. programming language, # developers, etc. 77
- 78. 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. 78
- 79. 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) 79
- 80. 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 Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers History Metrics Other Metrics Semi- supervised/active
- 81. Semi- supervised/active Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics History Metrics Just-In-Time Prediction Model Cross-Project Prediction Other Metrics Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers Personalized Model
- 82. 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 82 Source Target
- 83. 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 Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers Cross-Domain Prediction History Metrics Other Metrics Noise Reduction Semi- supervised/activePersonalized Model
- 84. Other Topics 84
- 85. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics History Metrics Just-In-Time Prediction Model Cross-Project Prediction Other Metrics Practical Model and Applications Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers Data Privacy Noise Reduction Semi- supervised/activePersonalized Model
- 86. 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) 86
- 87. 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 87
- 88. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Prediction Model (Regression) Prediction Model (Classification) Cyclomati c Metric Halstea d Metrics CK Metrics History Metrics Just-In-Time Prediction Model Cross-Project Prediction Other Metrics Practical Model and Applications Data Privacy Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers Noise Reduction Semi- supervised/activePersonalized Model
- 89. 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 Universa l Model Process Metrics Cross-Project Feasibility MetricsModelsOthers Cross-Domain Prediction Fine-grained Prediction Data Privacy Noise Reduction Semi- supervised/activePersonalized Model
- 90. Thank you! 90
- 91. 91
- 92. Evaluation Measures (classification) • Measures for binary classification – Confusion matrix 92 Buggy Clean Buggy True Positive (TP) False Negative (FN) Clean False Positive (FP) True Negatives (TN) Predicted Class Actual Class
- 93. Evaluation Measures (classification) • False positive rate (FPR,PF) = FP/(TN+FP) • Accuracy = (TP+TN)/(TP+FP+TN+FN) • Precision = TP/(TP+FP) • Recall = TP/(TP+FN) • F-measure = 2*Precision*Recall Precision+Recall 93
- 94. Evaluation Measures (classification) • AUC (Area Under receiver operating characteristic Curve) 94 False Positive rate TruePositiverate 0 1 1
- 95. Evaluation Measures (classification) • AUCEC (Area Under Cost Effectiveness Curve) 95 Percent of LOC Percentofbugsfound 0 100% 100% 50%10% M1 M2 Rahman@FSE`11, Bugcache for inspections: Hit or miss?
- 96. Evaluation Measures (Regression) • Target – Metric values vs. the number of bugs – Actual vs. predicted number of bugs • Correlation coefficient – Spearman / Pearson /R2 • Mean squared error 96
- 97. CK 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”) 97

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