Survey on Software Defect Prediction

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  • Cross-project change classification
    Feasibility evaluation on cross-project defect prediction
  • Predicting software quality
    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)
  • complex software itself, complex development process, even developers solving complex problems can introdue bugs on software
  • Depending on software, high recall might be more important than precision and vice versa.
  • Cross-project change classification
    Feasibility evaluation on cross-project defect prediction
  • V = N * log_2 n
    n = total # of distinct operands and operators
    N = total # of distinct operands and operators

    Correlation analysis using linear regression
  • V = N * log_2 n
    n = total # of distinct operands and operators
    N = total # of distinct operands and operators

    Correlation analysis using linear regression
  • Result from Command and Control Commutation System implanted in Ada

    Considered different thresholds for discriminative probability
  • Diffusion of change: How many file/modules/subsystems were touched together?
    Developer experience: # of previous changes by the same developer. Weighted by considering contributions of the set of developers.
  • Diffusion of change: How many file/modules/subsystems were touched together?
    Developer experience: # of previous changes by the same developer. Weighted by considering contributions of the set of developers.
  • When a bug is found but not in the BugCache, this is a cache miss.
    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.
  • complexity metrics of old and new revision files and then compute delta between the old and new.
  • complexity metrics of old and new revision files and then compute delta between the old and new.
  • 10 metrics are from Mockus`10

    Fukushima: cross-prediction performance can be improved
  • As there was a transition from fitting model to prediction model, we need another transition from JIT prediction mode to Online learning based JIT prediction models.
  • Are defect prediction models practical in industry?
  • As there was a transition from fitting model to prediction model, we need another transition from JIT prediction mode to Online learning based JIT prediction models.
  • Engstrom: found more defect selected by defect prediction results
  • WMC: A class with more member methods than its peers is considered to be more complex and therefore more error prone.
    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 –


  • Relative code change churn: e.g. churned LOC (the accumulative number of deleted and added lines between a base version and a new version of a source file) divided by Total LOC

    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.
  • 10% recall improvement for Zimmermann’s approach (All vs. CM)
    20% decrease of AIC, 200% increase of D^2 (Taba`10)
  • Tested on 5 external projects
    Average Within F-measure: 0.42
    Average Universal F-measure: 0.39

    Average Within AUC: 0.72
    Average Universal AUC: 0.72


  • In traditional machine learning, we build a learning system by using instances in a same domain. In our case, the same domain means the same project.
    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 a source instance = sM
    mass of test data = kmM
    kmM^2은 constant

  • i = index of instance
    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

  • Why cross results are different between NN and TNB? TNB doesn’t apply feature selection.
    TNB didn’t report within-results
  • In machine leaning, there is a feature extraction approach to reduce feature space of data set.
    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.
  • Here is an example showing how PCA and TCA works.
    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 density function
    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]
  • Based on these normalization techniques, we defined several normalization options for defect prediction data sets.
    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.
  • This is the preliminary results of some prediction combinations.
    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.
  • TCA+ provides decision rules to select suitable normalization option.

    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!
  • Then, how could we characterize data set?
    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.
  • To measure similarity, we compute statistical parameters from DIST set such as minimum, maximum, mean, standard deviation, and the # of instances.
    With these information, we creted decision rules
  • These are 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.
  • This decision tree shows precision in advance.
  • Successful criteria
    Precision > 0.5 and Recall > 0.7
  • As there was a transition from fitting model to prediction model, we need another transition from JIT prediction mode to Online learning based JIT prediction models.
  • Assumed projects in the same group have the similar distribution

    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



  • As there was a transition from fitting model to prediction model, we need another transition from JIT prediction mode to Online learning based JIT prediction models.
  • Cross-project change classification
    Feasibility evaluation on cross-project defect prediction
  • As there was a transition from fitting model to prediction model, we need another transition from JIT prediction mode to Online learning based JIT prediction models.
  • WMC: A class with more member methods than its peers is considered to be more complex and therefore more error prone.
    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 –


  • Survey on Software Defect Prediction

    1. 1. Survey on Software Defect Prediction - PhD Qualifying Examination - July 3, 2014 Jaechang Nam Department of Computer Science and Engineering HKUST
    2. 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. 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. 4. Ground Assumption • The more complex, the more defect- prone 4
    5. 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. 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. 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. 8. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model MetricsModelsOthers
    9. 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. 10. Defect Prediction Approaches 1970s 1980s 1990s 2000s 2010s LOC Simple Model Fitting Model Cyclomati c Metric Halstea d Metrics MetricsModelsOthers
    11. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 25. Change Classification (Kim@TSE`08) • Limitations – Heavy model (11,500 features) – Not validated on commercial software products. 25
    26. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 47. Defect Prediction for New Software Projects • Universal Defect Prediction Model • Simi-supervised / active learning • Cross-Project Defect Prediction 47
    48. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 60. Transfer Naive Bayes (cont.) (Ma@IST`12) • Transfer Naive Bayes – New prior probability – New conditional probability 60
    61. 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. 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. 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. 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. 65. TCA (cont.) 65 Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis Target domain data Source domain data
    66. 66. TCA (cont.) 66 TCA Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
    67. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 84. Other Topics 84
    85. 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. 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. 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. 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. 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. 90. Thank you! 90
    91. 91. 91
    92. 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. 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. 94. Evaluation Measures (classification) • AUC (Area Under receiver operating characteristic Curve) 94 False Positive rate TruePositiverate 0 1 1
    95. 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. 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. 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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