Software Defect Prediction on Unlabeled Datasets

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Jaechang Nam's PHD Defence

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  • Good afternoon, everyone!

    I’m JC. Thanks for coming to my PhD defence.

    The title of my thesis is Software Defect Prediction on Unlabeled Datasets.
  • General Question of software defect prediction is:
    Can we identify defect-prone software entities in advance?
    For example, by using defect prediction technique, we can predict whether a source code file is buggy or clean.

    After predicting defect-prone software entities, software quality assurance teams can effectively allocate limited resources for software testing and code review to develop reliable software product.
  • Here is Project A and some software entities. Let say these entities are source code files.
    I want to predict whether these files are buggy or clean.
    To do this, we need a prediction model.
    Since defect prediction models are trained by machine learning algorithms, we need labeled instances collected from previous releases.

    This is an labeled instance. An instance consists of features and labels.
    Various software metrics such as LoC, # of functions in a file, and # of authors touching a source file, are used as features for machine learning.
    Software metrics measure complexity of software and its development process
    Each instance can be labeled by past bug information.
    Software metrics and past bug information can be collected from software archives such as version control systems and bug report systems.
    With these labeled instances, we can build a prediction model and predict the unlabeled instances.

    This prediction is conducted within the same project. So, we call this Within-project defect prediction (WPDP).
    There are many studies about WPDP and showed good prediction performance. ( like prediction accuracy is 0.7.)
  • What if there are no labeled instances. This can happen in new projects and projects lacking in historical data.
    New projects do not have past bug information to label instances.
    Some projects also does not have bug information because of lacking in historical data from software archives.
    When I participated in an industrial project for Samsung electronics, it was really difficult to generate labeled instances because their software archives are not well managed by developers.
    So, in some real industrial projects, we may not generate labeled instances to build a prediction model.
    Without labeled instances, we can not build a prediction model.
    After experiencing this limitation form the industry, I decided to address this problem.
  • We define this problem as Software Defect Prediction on Unlabeled Datasets.
  • There are existing solutions to build a prediction model for unlabeled datasets.
    The first solution is cross-project defect prediction. We can reuse labeled instances from other projects.

  • Normalization gives all data values in the same scale. For example, we can make mean value of data set as 0 and standard deviation as 1.

    Normalization is also known to be helpful for classification algorithm. As many defect prediction models classify source code as buggy or clean. It is a classification problem. So we applied normalization for all training and test data sets.
  • 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.
  • 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.
  • 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.
  • These are decision rules.
    If mean and std is same, we assume that distributions between 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.
  • 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]
  • 8 software subjects
    ReLink (Wu et al.@FSE`11): 3 subjects
    26 source code metrics (features)
    Apache / OpenIntent Safe / ZXing
    Manually inspected defect data (Golden set)
    AEEEM (D’Ambros et al.@MSR`10): 5 subjects
    61 metrics (source code, churn, entropy metrics)
    Apache Lucene (LC) / Equinox (EQ) / Eclipse JDT / Eclipse PDE UI / Mylyn (ML)
    Machine learning algorithms
    Logistic regression
  • We report within-project prediction results.
    In Within prediction settings, we used 50:50 random splits, which is widely used in several literatures.
    We repeated 50:50 random splits 100 times
  • Wilcoxon-matched paired test
  • Wilcoxon-matched paired test
  • Wilcoxon-matched paired test
  • Various feature selection approaches can be applied
  • AEEEM: object- oriented (OO) metrics, previous-defect metrics, entropy met- rics of change and code, and churn-of-source-code metrics [4].
    MORPH: McCabe’s cyclomatic metrics, CK metrics, and other OO metrics [36].
    ReLink: code complexity metrics
    NASA: Halstead metrics and McCabe’s cyclomatic metrics, additional complexity metrics such as parameter count and percentage of comments
    SOFTLAB: Halstead metrics and McCabe’s cyclomatic metrics
  • Clustering: group instances that have higher metric values
    Labeling: label groups that have higher metrics values as buggy
    Metric and Instance selection: select more informative metrics and instances
  • Clustering: group instances that have higher metric values
    Labeling: label groups that have higher metrics values as buggy
    Metric and Instance selection: select more informative metrics and instances
  • Manual effort to decide threshold
    Literature
    Tuning machine: using known bugs, decide threshold values that minimize prediction error.
    Analysis of multiple releases
  • In case of lucene, all clusters are labeled as clean by expert.
    better results are bold-faced. (not a statistical testing. experiment conducted once)
  • Software Defect Prediction on Unlabeled Datasets

    1. 1. Software Defect Prediction on Unlabeled Datasets - PhD Thesis Defence - July 23, 2015 Jaechang Nam Department of Computer Science and Engineering HKUST
    2. 2. Software Defect Prediction • 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? (applications) – Quality assurance for large software (Akiyama@IFIP’71) – Effective resource allocation • Testing (Menzies@TSE`07, Kim@FSE`15) • Code review (Rahman@FSE’11) 2
    3. 3. 3 Predict Training ? ? Model Project A : Metric value : Buggy-labeled instance : Clean-labeled instance ?: Unlabeled instance Software Defect Prediction Related Work Munson@TSE`92, Basili@TSE`95, Menzies@TSE`07, Hassan@ICSE`09, Bird@FSE`11,D’ambros@EMSE112 Lee@FSE`11,...
    4. 4. What if labeled instances do not exist? 4 ? ? ? ? ? Project X Unlabeled Dataset ?: Unlabeled instance : Metric value Model New projects Projects lacking in historical data
    5. 5. This problem is... 5 ? ? ? ? ? Project X Unlabeled Dataset ?: Unlabeled instance : Metric value Software Defect Prediction on Unlabeled Datasets
    6. 6. Existing Solutions? 6 ? ? ? ? ? (New) Project X Unlabeled Dataset ?: Unlabeled instance : Metric value
    7. 7. Solution 1 Cross-Project Defect Prediction (CPDP) 7 ? ? ? ? ? Training Predict Model Project A (source) Project X (target) Unlabeled Dataset : Metric value : Buggy-labeled instance : Clean-labeled instance ?: Unlabeled instance Related Work Watanabe@PROMISE08, Turhan@EMSE`09 Zimmermann@FSE`09, Ma@IST`12, Zhang@MSR`14 Challenges Same metric set (same feature space) • Worse than WPDP • Heterogeneous metrics between source and target Only 2% out of 622 CPDP combinations worked. (Zimmermann@FSE`09)
    8. 8. Solution 2 Using Only Unlabeled Datasets 8 ? ? ? ? ? Project X Unlabeled Dataset Training Model Predict Related Work Zhong@HASE`04, Catal@ITNG`09 • Manual Effort Challenge Human-intervention
    9. 9. 9 Software Defect Prediction on Unlabeled Datasets Sub-problems Proposed Techniques CPDP comparable to WPDP? Transfer Defect Learning (TCA+) CPDP across projects with heterogeneous metric sets? Heterogeneous Defect Prediction (HDP) DP using only unlabeled datasets without human effort? CLAMI
    10. 10. 10 Software Defect Prediction on Unlabeled Datasets Sub-problems Proposed Techniques CPDP comparable to WPDP? Transfer Defect Learning (TCA+) CPDP across projects with heterogeneous metric sets? Heterogeneous Defect Prediction (HDP) DP using only unlabeled datasets without human effort? CLAMI
    11. 11. CPDP • Reason for poor prediction performance of CPDP – Different distributions of source and target datasets (Pan et al@TKDE`09) 11
    12. 12. TCA+ 12 Source Target Oops, we are different! Let’s meet at another world! (Projecting datasets into a latent feature space) New Source New Target Normalize US together!Normalization Transfer Component Analysis (TCA) + Make different distributions between source and target similar!
    13. 13. Data Normalization • Adjust all metric values in the same scale – E.g., Make Mean = 0 and Std = 1 • Known to be helpful for classification algorithms to improve prediction performance (Han@`12). 13
    14. 14. Normalization Options • N1: Min-max Normalization (max=1, min=0) [Han et al., 2012] • N2: Z-score Normalization (mean=0, std=1) [Han et al., 2012] • N3: Z-score Normalization only using source mean and standard deviation • N4: Z-score Normalization only using target mean and standard deviation • NoN: No normalization 14
    15. 15. Decision Rules for Normalization • Find a suitable normalization • Steps – #1: Characterize a dataset – #2: Measure similarity between source and target datasets – #3: Decision rules 15
    16. 16. Decision Rules for Normalization #1: Characterize a dataset 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 16
    17. 17. Decision Rules for Normalization #2: Measure Similarity between source and target 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 17 • Minimum (min) and maximum (max) values of DIST • Mean and standard deviation (std) of DIST • The number of instances
    18. 18. Decision Rules for Normalization #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) 18
    19. 19. TCA • Key idea Source Target New Source New Target Oops, we are different! Let’s meet at another world! (Projecting datasets into a latent feature space) 19
    20. 20. TCA (cont.) 20 Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis Target domain data Source domain data Buggy source instances Clean source instances Buggy target instances Clean target instances
    21. 21. TCA (cont.) 21 TCA Pan et al.@TNN`10, Domain Adaptation via Transfer Component Analysis
    22. 22. TCA+ 22 Source Target New Source New Target Normalize us together with a suitable option! Normalization Transfer Component Analysis (TCA) + Make different distributions between source and target similar! Oops, we are different! Let’s meet at another world! (Projecting datasets into a latent feature space)
    23. 23. EVALUATION 23
    24. 24. Research Questions • RQ1 – What is the cross-project prediction performance of TCA/TCA+ compared to WPDP? • RQ2 – What is the cross-project prediction performance of TCA/TCA+ compared to that CPDP without TCA/TCA+? 24
    25. 25. Experimental Setup • 8 software subjects • Machine learning algorithm – Logistic regression ReLink (Wu et al.@FSE`11) Projects # of metrics (features) Apache 26 (Source code) Safe ZXing AEEEM (D’Ambros et al.@MSR`10) Projects # of metrics (features) Apache Lucene (LC) 61 (Source code, Churn, Entropy,…) Equinox (EQ) Eclipse JDT Eclipse PDE UI Mylyn (ML) 25
    26. 26. Experimental Design Test set (50%) Training set (50%) Within-project defect prediction (WPDP) 26
    27. 27. Experimental Design Target project (Test set) Source project (Training set) Cross-project defect prediction (CPDP) 27
    28. 28. Experimental Design Target project (Test set) Source project (Training set) Cross-project defect prediction with TCA/TCA+ TCA/TCA+ 28
    29. 29. RESULTS 29
    30. 30. ReLink Result Representative 3 out of 6 combinations *CPDP: Cross-project defect prediction without 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 F-measure WPDP CPDP TCA TCA+ Safe  Apache Apache  Safe Safe  ZXing WPDP CPDP TCA TCA+ WPDP CPDP TCA TCA+ 30
    31. 31. ReLink Result F-measure Cross Source  Target Safe  Apache Zxing  Apache Apache  Safe Zxing  Safe Apache  ZXing Safe  ZXing Average CPDP 0.52 0.69 0.49 0.59 0.46 0.10 0.49 TCA 0.64 0.64 0.72 0.70 0.45 0.42 0.59 TCA+ 0.64 0.72 0.72 0.64 0.49 0.53 0.61 WPDP 0.64 0.62 0.33 0.53 *CPDP: Cross-project defect prediction without 31
    32. 32. AEEEM Result Representative 3 out of 20 combinations *CPDP: Cross-project defect prediction without TCA/TCA+ 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 F-measure WPDP CPDP TCA TCA+ JDT  EQ PDE  LC PDE  ML WPDP CPDP TCA TCA+ WPDP CPDP TCA TCA+ 32
    33. 33. AEEEM Result F-measure Cross Source  Target JDT  EQ LC  EQ ML  EQ … PDE  LC EQ  ML JDT  ML LC  ML PDE ML … Average CPDP 0.31 0.50 0.24 … 0.33 0.19 0.27 0.20 0.27 … 0.32 TCA 0.59 0.62 0.56 … 0.27 0.62 0.56 0.58 0.48 … 0.41 TCA+ 0.60 0.62 0.56 … 0.33 0.62 0.56 0.60 0.54 … 0.41 WPDP 0.58 … 0.37 0.30 … 0.42 33
    34. 34. Related Work 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`0 9 Ma@IST`12 Nam@ICSE`13 * NN = Nearest neighbor, W = Within, C = Cross 34
    35. 35. 35 Software Defect Prediction on Unlabeled Datasets Sub-problems Proposed Techniques CPDP comparable to WPDP? Transfer Defect Learning (TCA+) CPDP across projects with heterogeneous metric sets? Heterogeneous Defect Prediction (HDP) DP using only unlabeled datasets without human effort? CLAMI
    36. 36. Motivation 36 ? ? ? ? ? Training Test Model Project A (source) Project B (target) Same metric set (same feature space) CPDP In experiments of TCA+ Datasets in ReLink Datasets in AEEEMX Unlabeled Dataset Apache Safe JDTX
    37. 37. Motivation 37 ? Training Test Model Project A (source) Project C (target) ? ? ? ? ? ? ? Heterogeneous metric sets (different feature spaces or different domains) Possible to Reuse all the existing defect datasets for CPDP! Heterogeneous Defect Prediction (HDP)
    38. 38. Key Idea • Most defect prediction metrics – Measure complexity of software and its development process. • e.g. – The number of developers touching a source code file (Bird@FSE`11) – The number of methods in a class (D’Ambroas@ESEJ`12) – The number of operands (Menzies@TSE`08) More complexity implies more defect-proneness (Rahman@ICSE`13) 38
    39. 39. Key Idea • Most defect prediction metrics – Measure complexity of software and its development process. • e.g. – The number of developers touching a source code file (Bird@FSE`11) – The number of methods in a class (D’Ambroas@ESEJ`12) – The number of operands (Menzies@TSE`08) More complexity implies more defect-proneness (Rahman@ICSE`13) 39 Match source and target metrics that have similar distribution
    40. 40. Heterogeneous Defect Prediction (HDP) - Overview - 40 X1 X2 X3 X4 Label 1 1 3 10 Buggy 8 0 1 0 Clean ⋮ ⋮ ⋮ ⋮ ⋮ 9 0 1 1 Clean Metric Matching Source: Project A Target: Project B Cross- prediction Model Build (training) Predict (test) Metric Selection Y1 Y2 Y3 Y4 Y5 Y6 Y7 Label 3 1 1 0 2 1 9 ? 1 1 9 0 2 3 8 ? ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 0 1 1 1 2 1 1 ? 1 3 10 Buggy 8 1 0 Clean ⋮ ⋮ ⋮ ⋮ 9 1 1 Clean 1 3 10 Buggy 8 1 0 Clean ⋮ ⋮ ⋮ ⋮ 9 1 1 Clean 9 1 1 ? 8 3 9 ? ⋮ ⋮ ⋮ ⋮ 1 1 1 ?
    41. 41. Metric Selection • Why? (Guyon@JMLR`03) – Select informative metrics • Remove redundant and irrelevant metrics – Decrease complexity of metric matching combinations • Feature Selection Approaches (Gao@SPE`11,Shivaji@TSE`13) – Gain Ratio – Chi-square – Relief-F – Significance attribute evaluation 41
    42. 42. Metric Matching 42 Source Metrics Target Metrics X1 X2 Y1 Y2 0.8 0.5 * We can apply different cutoff values of matching score * It can be possible that there is no matching at all.
    43. 43. Compute Matching Score KSAnalyzer • Use p-value of Kolmogorov-Smirnov Test (Massey@JASA`51) 43 Matching Score M of i-th source and j-th target metrics: Mij = pij
    44. 44. Heterogeneous Defect Prediction - Overview - 44 X1 X2 X3 X4 Label 1 1 3 10 Buggy 8 0 1 0 Clean ⋮ ⋮ ⋮ ⋮ ⋮ 9 0 1 1 Clean Metric Matching Source: Project A Target: Project B Cross- prediction Model Build (training) Predict (test) Metric Selection Y1 Y2 Y3 Y4 Y5 Y6 Y7 Label 3 1 1 0 2 1 9 ? 1 1 9 0 2 3 8 ? ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ 0 1 1 1 2 1 1 ? 1 3 10 Buggy 8 1 0 Clean ⋮ ⋮ ⋮ ⋮ 9 1 1 Clean 1 3 10 Buggy 8 1 0 Clean ⋮ ⋮ ⋮ ⋮ 9 1 1 Clean 9 1 1 ? 8 3 9 ? ⋮ ⋮ ⋮ ⋮ 1 1 1 ?
    45. 45. EVALUATION 45
    46. 46. Baselines • WPDP • CPDP-CM (Turhan@EMSE`09,Ma@IST`12,He@IST`14) – Cross-project defect prediction using only common metrics between source and target datasets • CPDP-IFS (He@CoRR`14) – Cross-project defect prediction on Imbalanced Feature Set (i.e. heterogeneous metric set) – 16 distributional characteristics of values of an instance as features (e.g., mean, std, maximum,...) 46
    47. 47. Research Questions (RQs) • RQ1 – Is heterogeneous defect prediction comparable to WPDP? • RQ2 – Is heterogeneous defect prediction comparable to CPDP-CM? • RQ3 – Is Heterogeneous defect prediction comparable to CPDP-IFS? 47
    48. 48. Benchmark Datasets Group Dataset # of instances # of metrics Granularity All Buggy (%) AEEEM EQ 325 129 (39.7%) 61 Class JDT 997 206 (20.7%) LC 399 64 (9.36%) ML 1862 245 (13.2%) PDE 1492 209 (14.0%) MORP H ant-1.3 125 20 (16.0%) 20 Class arc 234 27 (11.5%) camel-1.0 339 13 (3.8%) poi-1.5 237 141 (75.0%) redaktor 176 27 (15.3%) skarbonka 45 9 (20.0%) tomcat 858 77 (9.0%) velocity-1.4 196 147 (75.0%) xalan-2.4 723 110 (15.2%) xerces-1.2 440 71 (16.1%) 48 Group Dataset # of instances # of metrics Granularity All Buggy (%) ReLink Apache 194 98 (50.5%) 26 FileSafe 56 22 (39.3%) ZXing 399 118 (29.6%) NASA cm1 327 42 (12.8%) 37 Function mw1 253 27 (10.7%) pc1 705 61 (8.7%) pc3 1077 134 (12.4%) pc4 1458 178 (12.2%) SOFTLA B ar1 121 9 (7.4%) 29 Function ar3 63 8 (12.7%) ar4 107 20 (18.7%) ar5 36 8 (22.2%) ar6 101 15 (14.9%) 600 prediction combinations in total!
    49. 49. Experimental Settings • Logistic Regression • HDP vs. WPDP, CPDP-CM, and CPDP-IFS 49 Test set (50%) Training set (50%) Project 1 Project 2 Project n ... ... X 1000 Project 1 Project 2 Project n ... ... CPDP-CM CPDP-IFS HDP WPDP Project A
    50. 50. Evaluation Measures • False Positive Rate = FP/(TN+FP) • True Positive Rate = Recall • AUC (Area Under receiver operating characteristic Curve) 50 False Positive rate TruePositiverate 0 1 1
    51. 51. Evaluation Measures • Win/Tie/Loss (Valentini@ICML`03, Li@JASE`12, Kocaguneli@TSE`13) – Wilcoxon signed-rank test (p<0.05) for 1000 prediction results – Win • # of outperforming HDP prediction combinations with statistical significance. (p<0.05) – Tie • # of HDP prediction combinations with no statistical significance. (p≥0.05) – Loss • # of outperforming baseline prediction results with statistical significance. (p>0.05) 51
    52. 52. RESULT 52
    53. 53. Prediction Results in median AUC Target WPDP CPDP- CM CPDP- IFS HDPKS (cutoff =0.05) EQ 0.583 0.776 0.461 0.783 JDT 0.795 0.781 0.543 0.767 MC 0.575 0.636 0.584 0.655 ML 0.734 0.651 0.557 0.692* PDE 0.684 0.682 0.566 0.717 ant-1.3 0.670 0.611 0.500 0.701 arc 0.670 0.611 0.523 0.701 camel-1.0 0.550 0.590 0.500 0.639 poi-1.5 0.707 0.676 0.606 0.537 redaktor 0.744 0.500 0.500 0.537 skarbonka 0.569 0.736 0.528 0.694* tomcat 0.778 0.746 0.640 0.818 velocity- 1.4 0.725 0.609 0.500 0.391 xalan-2.4 0.755 0.658 0.499 0.751 xerces-1.2 0.624 0.453 0.500 0.489 53 Target WPDP CPDP- CM CPDP- IFS HDPKS (cutoff =0.05) Apache 0.714 0.689 0.635 0.717* Safe 0.706 0.749 0.616 0.818* ZXing 0.605 0.619 0.530 0.650* cm1 0.653 0.622 0.551 0.717* mw1 0.612 0.584 0.614 0.727 pc1 0.787 0.675 0.564 0.752* pc3 0.794 0.665 0.500 0.738* pc4 0.900 0.773 0.589 0.682* ar1 0.582 0.464 0.500 0.734* ar3 0.574 0.862 0.682 0.823* ar4 0.657 0.588 0.575 0.816* ar5 0.804 0.875 0.585 0.911* ar6 0.654 0.611 0.527 0.640 All 0.657 0.636 0.555 0.724* HDPKS: Heterogeneous defect prediction using KSAnalyzer
    54. 54. Win/Tie/Loss Results Target Against WPDP Against CPDP-CM Against CPDP-IFS W T L W T L W T L EQ 4 0 0 2 2 0 4 0 0 JDT 0 0 5 3 0 2 5 0 0 LC 6 0 1 3 3 1 3 1 3 ML 0 0 6 4 2 0 6 0 0 PDE 3 0 2 2 0 3 5 0 0 ant-1.3 6 0 1 6 0 1 5 0 2 arc 3 1 0 3 0 1 4 0 0 camel-1.0 3 0 2 3 0 2 4 0 1 poi-1.5 2 0 2 3 0 1 2 0 2 redaktor 0 0 4 2 0 2 3 0 1 skarbonka 11 0 0 4 0 7 9 0 2 tomcat 2 0 0 1 1 0 2 0 0 velocity- 1.4 0 0 3 0 0 3 0 0 3 xalan-2.4 0 0 1 1 0 0 1 0 0 xerces-1.2 0 0 3 3 0 0 1 0 2 54 Target Against WPDP Against CPDP-CM Against CPDP-IFS W T L W T L W T L Apach e 6 0 5 8 1 2 9 0 2 Safe 14 0 3 12 0 5 15 0 2 ZXing 8 0 0 6 0 2 7 0 1 cm1 7 1 2 8 0 2 9 0 1 mw1 5 0 1 4 0 2 4 0 2 pc1 1 0 5 5 0 1 6 0 0 pc3 0 0 7 7 0 0 7 0 0 pc4 0 0 7 2 0 5 7 0 0 ar1 14 0 1 14 0 1 11 0 4 ar3 15 0 0 5 0 10 10 2 3 ar4 16 0 0 14 1 1 15 0 1 ar5 14 0 4 14 0 4 16 0 2 ar6 7 1 7 8 4 3 12 0 3 Total 147 3 72 147 14 61 182 3 35 % 66.2 % 1.4% 32.4 % 66.2 % 6.3% 27.5 % 82.0 % 1.3% 16.7 %
    55. 55. Matched Metrics (Win) 55 MetricValues Distribution (Source metric: RFC-the number of method invoked by a class, Target metric: the number of operand Matching Score = 0.91 AUC = 0.946 (ant1.3  ar5)
    56. 56. Matched Metrics (Loss) 56 MetricValues Distribution (Source metric: LOC, Target metric: average number of LOC in a method) Matching Score = 0.13 AUC = 0.391 (Safe  velocity-1.4)
    57. 57. Different Feature Selections (median AUCs, Win/Tie/Loss) 57 Approach Against WPDP Against CPDP-CM Against CPDP-IFS HDP AUC Win% AUC Win% AUC Win% AUC Gain Ratio 0.657 63.7% 0.645 63.2% 0.536 80.2% 0.720 Chi-Square 0.657 64.7% 0.651 66.4% 0.556 82.3% 0.727 Significanc e 0.657 66.2% 0.636 66.2% 0.553 82.0% 0.724 Relief-F 0.670 57.0% 0.657 63.1% 0.543 80.5% 0.709 None 0.657 47.3% 0.624 50.3% 0.536 66.3% 0.663
    58. 58. Results in Different Cutoffs 58 Cutoff Against WPDP Against CPDP-CM Against CPDP-IFS HDP Target Coverage AUC Win% AUC Win% AUC Win% AUC 0.05 0.657 66.2% 0.636 66.2% 0.553 82.4% 0.724* 100% 0.90 0.657 100% 0.761 71.4% 0.624 100% 0.852* 21%
    59. 59. 59 Software Defect Prediction on Unlabeled Datasets Sub-problems Proposed Techniques CPDP comparable to WPDP? Transfer Defect Learning (TCA+) CPDP across projects with heterogeneous metric sets? Heterogeneous Defect Prediction (HDP) DP using only unlabeled datasets without human effort? CLAMI
    60. 60. Motivation 60 - Loss result of HDP
    61. 61. Motivation 61 - Loss result of HDP Still difficult to make different distribution similar!
    62. 62. Motivation 62 Training Predict Unlabeled Dataset What if.... ?
    63. 63. How? • Recall the trend of defect prediction metrics – Measures complexity of software and its development process. • e.g. – The number of developers touching a source code file (Bird@FSE`11) – The number of methods in a class (D’Ambroas@ESEJ`12) – The number of operands (Menzies@TSE`08) Higher metric values imply more defect-proneness (Rahman@ICSE`13) 63
    64. 64. How? • Recall this trend of defect prediction metrics – Measures complexity of software and its development process. • e.g. – The number of developers touching a source code file (Bird@FSE`11) – The number of methods in a class (D’Ambroas@ESEJ`12) – The number of operands (Menzies@TSE`08) Higher metric values imply more defect-proneness (Rahman@ICSE`13) 64 (1) Label instances that have higher metric values as buggy! (2) Generate a training set by removing metrics and instances that violates (1).
    65. 65. CLAMI Approach Overview 65 Unlabeled Dataset (1) Clustering (2) LAbeling (3) Metric Selection (4) Instance Selection (5) Metric Selection CLAMI Model Build Predict Training dataset Test dataset
    66. 66. CLAMI Approach - Clustering and Labeling Clusters - 66 Cluster, K=3 Unlabeled Dataset X1 X2 X3 X4 X5 X6 X7 Label 3 1 3 0 5 1 9 ? 1 1 2 0 7 3 8 ? 2 3 2 5 5 2 1 ? 0 0 8 1 0 1 9 ? 1 0 2 5 6 10 8 ? 1 4 1 1 7 1 1 ? 1 0 1 0 0 1 7 ? 1 1 2 1 5 1 8Median Inst. A Inst. B Inst. C Inst. D Inst. E Inst. F inst. G Instance s K = the number of higher metric values that are greater than Median. C Cluster, K=4 A, E B, D, F Cluster, K=2 G Cluster, K=0 (1) Clustering (2) Labeling Clusters Higher values : buggy clusters : clean clusters
    67. 67. CLAMI Approach - Metric Selection - 67 {X1,X4} X1 X2 X3 X4 X5 X6 X7 Label 3 1 3 0 5 1 9 Buggy 1 1 2 0 7 3 8 Clean 2 3 2 5 5 2 1 Buggy 0 0 8 1 0 1 9 Clean 1 0 2 5 6 10 8 Buggy 1 4 1 1 7 1 1 Clean 1 0 1 0 0 1 7 Clean Inst. A Inst. B Inst. C Inst. D Inst. E Inst. F Inst. G 1 3 3 1 4 2 3 # of Violations Selected Metrics Violation: a metric value that does not follow its label! Higher values are bold-facedViolations
    68. 68. CLAMI Approach - Instance Selection - 68 X1 X4 Label 3 0 Buggy 1 0 Clean 2 5 Buggy 0 1 Clean 1 5 Buggy 1 1 Clean 1 0 Clean Inst. A Inst. B Inst. C Inst. D Inst. E Inst. F Inst. G X1 X4 Label 1 0 Clean 2 5 Buggy 0 1 Clean 1 1 Clean 1 0 Clean Inst. B Inst. C Inst. D Inst. F Inst. G Final Training Dataset
    69. 69. CLAMI Approach Overview 69 Unlabeled Dataset (1) Clustering (2) LAbeling (3) Metric Selection (4) Instance Selection (5) Metric Selection CLAMI Model Build Predict Training dataset Test dataset
    70. 70. EVALUATION 70
    71. 71. Baselines • Supervised learning model (i.e. WPDP) • Defect prediction only using unlabeled datasets – Expert-based (Zhong@HASE`04) • Cluster instances by K-Mean into 20 clusters • A human expert labels each cluster – Threshold-based (Catal@ITNG`09) • [LoC, CC, UOP, UOpnd, TOp, TOpnd] = [65, 10, 25, 40, 125, 70] – Label an instance whose any metric value is greater than a threshold value • Manual effort requires to decide threshold values in advance. 71
    72. 72. Research Questions (RQs) • RQ1 – CLAMI vs. Supervised learning model? • RQ2 – CLAMI vs. Expert-/threshold-based approaches? (Zhong@HASE`04, Catal@ITNG`09) 72
    73. 73. Benchmark Datasets Group Dataset # of instnaces # of metrics Prediction GranularityAll Buggy (%) NetGene Httpclient 361 205 (56.8%) 465 (Network, Change genealogy) File Jackrabbit 542 225 (41.5%) Lucene 1671 346 (10.7%) Rhino 253 109 (43.1%) ReLink Apache 194 98 (50.5%) 26 (code complexity) File Safe 56 22 (39.29%) ZXing 399 118 (29.6%) 73
    74. 74. Experimental Settings (RQ1) - Supervised learning model - 74 Test set (50%) Training set (50%) Supervised Model (Baseline) Training Predict X 1000 CLAMI Model Training Predict
    75. 75. Experimental Settings (RQ2) -Comparison to existing approaches - 75 Unlabeled Dataset CLAMI Model Predict Training Predict Threshold- Based (Baseline1, Catal@ITNG`09) Expert- Based (Baseline2, Zhong@HASE`04)
    76. 76. Measure • F-measure • AUC 76
    77. 77. RESULT 77
    78. 78. Supervised model vs. CLAMI Dataset F-measure AUC Supervise d (w/ labels) CLAMI (w/o labels) +/-% Supervise d (w/ labels) CLAMI (w/o labels) +/-% Httpclient 0.729 0.722 -1.0% 0.727 0.772 +6.2% Jackrabbi t 0.649 0.685 +5.5% 0.727 0.751 +3.2% Lucene 0.508 0.397 -21.8% 0.708 0.595 -15.9% Rhino 0.639 0.752 +17.7% 0.702 0.777 +10.7% Apache 0.653 0.720 +10.2% 0.714 0.753 +5.3% Safe 0.615 0.667 +8.3% 0.706 0.773 +9.5% ZXing 0.333 0.497 +49.0% 0.605 0.644 +6.4% Median 0.639 0.685 +7.2% 0.707 0.753 +6.3% 78
    79. 79. Existing approaches vs. CLAMI f-measure Dataset Threshold-based Expert-based CLAMI Httpclient 0.355 0.811 0.756 Jackrabbit 0.184 0.676 0.685 Lucene 0.144 0.000 0.404 Rhino 0.190 0.707 0.731 Apache 0.547 0.701 0.725 Safe 0.308 0.718 0.694 ZXing 0.228 0.402 0.505 Median 0.228 0.701 0.694 79
    80. 80. Distributions of metrics (Safe) 80 Most frequently selected metrics by CLAMI Metrics with less discriminative power
    81. 81. Distributions of metrics (Lucene) 81 Most frequently selected metrics by CLAMI Metrics with less discriminative power
    82. 82. 82 Software Defect Prediction on Unlabeled Datasets Sub-problems Proposed Techniques CPDP comparable to WPDP? Transfer Defect Learning (TCA+) CPDP across projects with heterogeneous metric sets? Heterogeneous Defect Prediction (HDP) DP using only unlabeled datasets without human effort? CLAMI
    83. 83. Conclusion 83 Sub-problems Technique 1: TCA+ Technique 2: HDP Technique 3: CLAMI Comparable prediction performance than WPDP O (in f-measure) O (in AUC) O Able to handle heterogeneous metric sets X O O Automated without human effort O O O
    84. 84. Publications at HKUST • Defect Prediction – Micro Interaction Metrics for Defect Prediction@FSE`11, Taek Lee, Jaechang Nam, Donggyun Han, Sunghun Kim and Hoh Peter In – Transfer Defect Learning@ICSE`13, Jaechang Nam, Sinno Jialin Pan and Sunghun Kim, Nominee, ACM SIGSOFT Distinguished Paper Award – Heterogeneous Defect Prediction@FSE`15, Jaechang Nam ann Sunghun Kim – REMI: Defect Prediction for Efficient API Testing@FSE`15, Mijung Kim, Jaechang Nam, Jaehyuk Yeon, Soonhwang Choi, and Sunghun Kim, Industrial Track – CLAMI: Defect Prediction on Unlabeled Datasets@ASE`15, Jaechang Nam and Sunghun Kim • Testing – Calibrated Mutation Testing@MUTATION`12, Jaechang Nam, David Schuler, and Andreas Zeller • Automated bug-fixing – Automatic Patch Generation Learned from Human-written Patches@ICSE`13, Dongsun Kim, Jaechang Nam, Jaewoo Song and Sunghun Kim, ACM SIGSOFT Distinguished Paper Award Winner 84
    85. 85. Cross- Prediction Feasibility Check CLAMI NoSame metric set? TCA+ Feasibl e? Yes No Yes HDP Unlabeled Project Dataset Existing Labeled Project Datasets Ensemble model for defect prediction on unlabeled datasets 85
    86. 86. Q&A THANK YOU! 86

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