1. CSE 300: Software Reliability Engineering Topics covered: Software metrics and software reliability

2. Introduction

3.  What can be measured?  Predicted quality attributes:

4. Static complexity metrics  Measurements on:  Obtained earlier in the life cycle

5. Halstead’s software science metrics  Primitive metrics:  Composite, non primitive metrics:

6. Halstead’s software science metrics (contd..)  Discussion:

7. McCabe’s cyclomatic complexity metric

8.  Application

9. Principal Components Analysis  Transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components  First principal component accounts for as much variability in the data as possible.  Each subsequent component accounts for as much remaining variability as possible.  Principal components represent transformed scores on dimensions that are orthogonal  Decomposition technique to detect and analyze relationships among variables  Identify distinct sources of variation underlying the set of variables

10. Principal Components Analysis  Application:  Many metrics exist to measure the same artifact.  Metrics are interrelated.  Reduce the large set of correlated metrics to a small set of uncorrelated variables, which capture the same information.  Investigate the structure of the underlying common factors or components that make up the raw metrics.

11. Steps in Principal Components Analysis  Data:  Software metrics data  Step I: Organize the data in the form of n x m matrix, where n is the number of modules and m is the number of metrics.

12. Steps in Principal Components Analysis  Subtract the mean from each one of the metrics observations

13. Steps in Principal Components Analysis  Step III: Compute the covariance matrix. Covariance matrix will be m x m.

14. Steps in Principal Components Analysis  Step IV: Compute the eigenvectors and eigenvalues

15. Steps in Principal Component Analysis  Step V: Choosing components and forming a feature vector.

16. Steps in Principal Component Analysis  Step VI: Deriving the new data set:

17. Steps in Principal Component Analysis  Step VII: Getting the old data back: