The document summarizes a research paper that empirically validated several object-oriented design metrics proposed by Chidamber and Kemerer as indicators of fault-prone classes. The study analyzed 6 metrics on 180 classes from a system. Univariate analysis found 5 metrics to be significantly correlated with fault probability. Multivariate analysis using these 5 metrics achieved better prediction of faulty classes than models using traditional code metrics. The research validated that these OO design metrics can help identify fault-prone classes early in the development lifecycle.

1. A Validation of Object-Oriented Design Metrics as Quality Indicators Evi Yulianti 1006833110 Iis Solichah 1006800094 Mubarik Ahmad 1006833294  Victor R. Basili  Lionel C. Briand  Walcelio L. Melo University of Mariland

2. Content Article & Author Introduction Data Analysis Case Study Conclusion & Future Work

3. Article Publication: Journal IEEE Transactions on Software Engineering Issue Date: Oct 1996 Volume: 22 Issue:10 On page(s): 751 - 761

4. Author Dr. Victor R. Basili (University of Maryland) Department of Computer Science , Professor, 1970 – Present Institute for Advanced Computer Studies , Research Professor, 1984-Present Dr. Lionel C. Briand (Carleton University) Canada Research Chair (Tier I) in Software Quality Engineering Dr. Walcelio (Walt) L. Melo, Professor, Catholic University of Brasilia, DF, Brazil , 1997-2001 Lead Architect, Model Driven Solutions, 2008-now

5. Introduction

6. Introduction Time & resource consuming activity help manager : 1. make decisions, plan and schedule activities, 2. allocate resources for the different software activities identify fault-prone modules TESTING SOFTWARE METRIC … ?

7. Introduction (cont’) Metrics must be defined and validated in order to be used in industry Empirical validation aims at demonstrating the usefulness of a measure in practice and is, therefore, a crucial activity to establish the overall validity of a measure. ability to identify fault-prone classes

8. Chidamber & Kemerer’s metric [13] Weighted Methods per Class (WMC) Depth of Inheritance Tree of a class (DIT) Number Of Children of a Class (NOC) Coupling Between Object classes (CBO) Response For a Class (RFC) Lack of Cohesion on Methods (LCOM)

9. Hypothesis H-WMC H-DIT H-NOC H-CBO H-RFC H-LCOM

10. Data Analysis

11. DATA ANALYSIS Assess empirically whether the OO design metrics defined in [13] are useful predictors or fault-prone classes. Used analysis: Descriptive distributions of the OO Metrics Univariate and multivariate analysis of the relationship between OO Metrics and fault-proneness.

12. Distribution and Correlation Analysis Distribution of the analyzed OO metrics based on 180 presented classes. Number of Class

13. Distribution and Correlation Analysis (cont.) Descriptive statistics of the metric distributions.

14. Distribution and Correlation Analysis (cont.) Linear Pearson's correlations (R': Coefficient of determination) between the studied OO metrics are very weak. These metrics are mostly statistically independent.

15. The Relationships Between Fault Probability and OO Metrics Analysis Methodology Explanatory variable: metrics from [13] Response variable: was a fault detected in a class during testing phases? (binary) Used methods: Logistic regression Univariate Multivariate

16. The Relationships Between Fault Probability and OO Metrics (cont.) Resulting Table

17. The Relationships Between Fault Probability and OO Metrics (cont.) Statistics: Coefficient: the estimated regression coefficient.  Statistical significance (p-value)

18. Univariate Analysis A nalyze the relationships between six OO metrics introduced in [ 13 ] and the probability of fault detection in a class during test phases. Test the hypothesis.

19. Univariate Analysis (cont.) Test Result: (analyzed from Table 3) WMC : H-WMC is supported. DIT : H-DIT is supported. RFC : H-RFC is supported. NOC : H-NOC is not supported. LCOM : can’t be analyzed. CBO : H-CBO is supported.

20. Multivariate Analysis T o evaluate the predictive capability of those metrics that had been assessed sufficiently significant in the univariate analysis. O nly the metrics that significantly improve the predictive power of the multivariate model

21. Result 1 The figures before parentheses i n the right column are the number of classes class i f i ed as faulty The figures within the parentheses are the faults contained i n those classes. Based on OO Design Metrics [13] Using such a model for classification, the results are obtained by using a classification threshold of π (Fault detection) = 0.5, i.e., when π > 0.5, the class is classified as faulty and, otherwise, as nonfaulty.

22. Result II Based on Code Metrics [2] 112 classes (predicted as faulty) out of 180 would be inspected and 51 faulty classes out of 58 would be detected

23. Result III Accuracies of OO and Code Metrics C orrectness (percentage of classes correctly predicted as faulted) C ompleteness(percentage of faulty classes detected) Values between parentheses present predictions correctness and completeness when classes are weighted according to number of faults they contain

24. CASE STUDY: DATA ANALYSIS FOR SAMPLE CLASSES

25. Class Diagram

26. Distribution Analysis Distribution of the analyzed OO metrics based on eight sample classes from “Tugas 1”.

27. Distribution Analysis (cont.) Distribution of the analyzed OO metrics based on eight sample classes from “Tugas 1”.

28. Distribution Analysis (cont.) Descriptive statistics of the metric value distributions. WMC DIT RFC NOC LCOM CBO Maximum 15 2 18 2 93 2 Minimum 1 0 1 0 0 0 Mean 6.875 0.5 10.375 0.375 11.625 1.375

29. Logistic Regression It is used for prediction of the probability of occurrence of an event It makes use of several predictor variables Multivariate logistic regression function: Univariate logistic regression is special case where only one variable appears

30. SPSS V.15 VARIABLE VIEW

31. DATA VIEW

32. Univariate Analysis:

33. coefficient constant z = - 0,513 + 0,075*WMC R 2 Odds ratio π = exp(z) / (1+exp(z))

34. WMC z= -0,513 + 0,075*WMC π = exp(z) / (1+exp(z)) Class WMC π Tool.java 1 0,392218 CTextbox.java 1 0,392218 DrawingPackage.java 3 0,428494 Screen.java 5 0,465555 CCircle.java 8 0,521736 ShapeList.java 10 0,558974 CRect.java 12 0,59556 CShape.java 15 0,648397

35. DIT z= -1,386 + 21,566*DIT π = exp(z) / (1+exp(z)) Class DIT π Tool.java 0 0,200047 Screen.java 0 0,200047 ShapeList.java 0 0,200047 CShape.java 0 0,200047 DrawingPackage.java 0 0,200047 CCircle.java 1 1 CRect.java 1 1 CTextbox.java 2 1

36. NOC z= 0,196 - 0,5309*NOC π = exp(z) / (1+exp(z)) Class NOC π Tool.java 0 0,548844 Screen.java 0 0,548844 ShapeList.java 0 0,548844 CCircle.java 0 0,548844 CTextbox.java 0 0,548844 DrawingPackage.java 0 0,548844 CRect.java 1 0,417049 CShape.java 2 0,296129

37. CBO z= -2,884 – 2,027*CBO π = exp(z) / (1+exp(z)) Class CBO π Screen.java 0 0,05295 Tool.java 1 0,297967 ShapeList.java 1 0,297967 CShape.java 1 0,297967 CCircle.java 2 0,763145 CRect.java 2 0,763145 CTextbox.java 2 0,763145 DrawingPackage.java 2 0,763145

38. RFC z= -0,941 + 0,09*RFC π = exp(z) / (1+exp(z)) Class RFC π Tool.java 1 0,299223 Screen.java 5 0,379658 CTextbox.java 6 0,401072 CCircle.java 9 0,467297 CRect.java 14 0,579081 CShape.java 15 0,600848 DrawingPackage.java 15 0,600848 ShapeList.java 18 0,663515

39. LCOM z= 0,288 - 0,231*LCOM π = exp(z) / (1+exp(z)) Class LCOM π Tool.java 0 0,571506429 Screen.java 0 0,571506429 ShapeList.java 0 0,571506429 CCircle.java 0 0,571506429 CRect.java 0 0,571506429 CTextbox.java 0 0,571506429 DrawingPackage.java 0 0,571506429 CShape.java 93 6,23919E-10

40. Multivariate Analysis:

41. constant coefficient WMC coefficient DIT coefficient NOC coefficient CBO coefficient RFC R 2 z= 50,930 - 4,249*WMC - 33,403*DIT + 28,433*NOC + 5,256*CBO -1,885*RFC π = exp(z) / (1+exp(z))

42. Related Works

43. CONCLUSION & FUTURE WORK

44. Conclusions F ive out of the six Chidamber and Kemerer’s OO metrics appear to be useful to predict class fault-proneness during the high- and low-level design phases of the life-cycle Chidamber and Kemerer’s OO metrics show to be better predictors than the best set of ”traditional” code metrics, which can only be collected during later phases of the software development processes. M ost of these metrics appear to be complementary indicators which are relatively independent from each other

45. Future Works Replicating this study in an industrial setting: A sample of large-scale projects developed in C++ and Ida95 in the framework of the NAsA Goddard Flight Dynamics Division (Software Engineering Laboratory). Studying the variations, in terms of metric definitions and experimental results, between different OO programming languages. Extending the empirical investigation to other OO metrics proposed in the literature and develop improved metrics

46. Paper Reference List [2] Amadeus Software Research, Getting Started With Amadeus, Amadeus Measurement System, 1994. [13] S.R. Chidamber and C.F. Kemerer, “A Metrics Suite for Object-Oriented Design,” IEEE Trans. Software Eng., vol. 20, no. 6, pp. 476493, June 1994.