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Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces
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Metrics - Using Source Code Metrics to Predict Change-Prone Java Interfaces

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Paper title: Using Source Code Metrics to Predict Change-Prone Java Interfaces …

Paper title: Using Source Code Metrics to Predict Change-Prone Java Interfaces

Authors: Daniele Romano and Martin Pinzger

Session: Research Track Session 11: Metrics

Published in: Technology
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  • 1. Using Source Code Metrics to Predict Change-Prone Java InterfacesDaniele Romano and Martin Pinzger Williamsburg, ICSM 201129 Sept 2011 Delft University of Technology Challenge the future
  • 2. Contributions•  Correlation source code metrics vs #changes in interfaces: •  C&K metrics •  complexity and usage metrics •  interface usage cohesion metric•  Predictive power of source code metrics for interfaces: •  prediction models•  10 open source projects •  8 Eclipse projects •  Hibernate 2 and Hibernate 3 2
  • 3. Motivations•  Changes in interfaces are not desirable •  changes can have stronger impact •  interfaces define contracts •  existing object oriented metrics not sound for interfaces •  Related work about metrics as quality predictors •  no differences among the kind of class 3
  • 4. Hypotheses•  H1 • InterfaceUsageCohesion (IUC) has a stronger correlation with number of Source Code Changes (#SCC) of interfaces than the C&K metrics•  H2 • IUC can improve the performance of prediction models to classify Java interfaces into change- and not- change-prone 4
  • 5. The Approach source code repository metrics Changes computation RetrievalSpearman rank Correlation Metrics train models correlation Prediction analysis Changes classify interfaces analysis H1 H2 5
  • 6. Metrics Computation Evolizer Modelsource code Importer repository Famix Model Computation Metrics Values Understand 6
  • 7. Changes Computation Evolizer source code Version Control repository Connector Revisions Info & Subsequent Changes Computation filesFine-Grained Evolizer ChangeSource Code Distiller Changes (SCC) AST Comparison 7
  • 8. Why SCC?•  Filtering out useless changes due to modification of: •  licenses •  comments•  More precise measurement#Revision=1 #LineModified=1 #SCC=2 8
  • 9. C&K Correlation for Interfaces Project CB0 NOC RFC DIT LCOM WMCHibernate3 0.535** 0.029 0.592** 0.058 0.103 0.657**Hibernate2 0.373** 0.065 0.325** -0.01 0.006 0.522**ecl.debug.core 0.484** 0.105 0.486** 0.232* 0.337 0.597**ecl.debug.ui 0.216* 0.033 0.152 0.324** 0.214* 0.131ecl.jface 0.239* 0.012 0.174** 0.103 0.320** 0.137ecl.jdt.debug 0.512** 0.256** 0.349** -0.049 0.238** 0.489**ecl.team.core 0.367* 0.102 0.497** 0.243 0.400 0.451**ecl.team.cvs.core 0.688** -0.013 0.738** 0.618** 0.610** 0.744**ecl.team.ui 0.301* -0.003 0.299* -0.103* 0.395** 0.299*update.core 0.499** -0.007 0.381** 0.146 0.482** 0.729** Median 0.428 0.031 0.365 0.124 0.328 0.505 *= significant at α=0.05 **= significant at α=0.01 9
  • 10. Weighted Methods per Class (WMC) •  ci cyclomatic complexity of the ith method •  n number of methods in a class Number of Methods 10
  • 11. Interface Segregation Principle  ISP   defined by Robert C. Martin   cope with fat interfaces  Fat interface   interfaces that serve different clients   each kind of client uses a different set of methods   the interface should be split in more interface, each one designed to serve a specific client 11
  • 12. Interface Segregation Principle (I) Different clients do not share any methodsClusterClients(i): counts the number of clientsthat do not share any method of the interface i 12
  • 13. Interface Usage Cohesion Different clients share a method 13
  • 14. Other metrics for interfaces…•  Number Of Methods (NOM)•  Number Of Arguments (NOA)•  Arguments Per Procedure (APP)•  Number of Clients (Cli)•  Number of Invocations (Inv)•  Number of Implementing Classes (Impl) 14
  • 15. Correlation for Interfaces Project Inv Cli NOM Clust IUCHibernate3 0.544** 0.433** 0.657** 0.302** -0.601**Hibernate2 0.165 0.104 0.522** 0.016 -0.373**ecl.debug.core 0.317** 0.327** 0.597** 0.273** -0.682**ecl.debug.ui 0.497** 0.498** 0.131 0.418** -0.508**ecl.jface 0.205 0.099 0.137 0.106** -0.363**ecl.jdt.debug 0.495** 0.471 0.489** 0.474** -0.605**ecl.team.core 0.261 0.278 0.451** 0.328* -0.475**ecl.team.cvs.core 0.557** 0.608** 0.744** 0.369 -0.819**ecl.team.ui 0.290 0.270 0.299 0.056 -0.618**update.core 0.677** 0.656** 0.729** 0.606** -0.656** Median 0.317 0.327 0.505 0.328 -0.605 *= significant at α=0.05 **= significant at α=0.01 15
  • 16. Prediction Analysis•  Three Machine Learning Algorithms •  upport Vector Machine S •  aïve Bayes Network N •  eural Nets N•  Interfaces classification:•  Training using 10 fold cross-validation •  {CBO, RFC, LCOM, WMC} = CK •  {CBO, RFC, LCOM, WMC, IUC} = IUC 16
  • 17. Prediction – AUC values NBayes LibSVN NN Project CK IUC CK IUC CK IUCecl.team.cvs.core 0.55 0.75 0.692 0.811 0.8 0.8ecl.debug.core 0.75 0.79 0.806 0.828 0.85 0.875ecl.debug.ui 0.66 0.72 0.71 0.742 0.748 0.766Hibernate2 0.745 0.807 0.735 0.708 0.702 0.747Hibernate3 0.835 0.862 0.64 0.856 0.874 0.843ecl.jdt.debug 0.79 0.738 0.741 0.82 0.77 0.762ecl.jface 0.639 0.734 0.607 0.778 0.553 0.542ecl.team.core 0.708 0.792 0.617 0.608 0.725 0.85ecl.team.ui 0.88 0.8 0.74 0.884 0.65 0.75update.core 0.782 0.811 0.794 0.817 0.675 0.744 Median 0.747 0.791 0.722 0.814 0.736 0.764 17
  • 18. Results•  H1 ACCEPTED • IUC has a stronger correlation with #SCC of interfaces than the C&K metrics •  UIC shows the best correlation•  H2 PARTIALLY ACCEPTED • IUC can improve the performance of prediction models to classify Java interfaces into change- and not- change-prone •  Despite the improvements Wilcoxon test showed a significant difference only for the LibSVM 18
  • 19. Implications• Researchers •  taking in account the nature of the measured entities• Quality Engineers •  enlarge metrics suites• Developers and Architects •  Measure the ISP violation 19
  • 20. Future Work• Metrics measurement overtime• Further validation• Are the shared methods the problem?• Component Based System and Service Oriented System 20
  • 21. 21

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