CSE 300: Software Reliability Engineering Topics covered: Software metrics and software reliability Software complexity and software quality

Introduction

Software quality models  Software quality model:  Multivariate techniques:  Use of software quality models:

Multiple discriminant analysis  Classification technique:  Derives a linear combination of independent variables that discriminates between the a priori groups such that misclassification error rates are minimized.

Multiple discriminant analysis  Introduction:  Technique for classifying a set of observations into predefined classes  Determine the class of an observation based on a set of predictor or input variables  Build a model for a set of observations for which the classes are known. Set known as training set  Using the training set, the technique constructs a set of linear functions of predictors such that L = b1x1 + …. + c Where b1, b2, b3,.. Are discriminant coefficients, and c is a constant. Discriminant functions used to predict the membership of an observation with a unknown class. Assign an observation to a discriminant with the highest value.

Multiple discriminant analysis  Many techniques:  Independent variables are uncorrelated:

Multiple discriminant analysis: Quality model  Objects are program modules  Independent measures could be measures of complexity  Predefined classes based on certain criteria:  Complexity metrics could be correlated:  Derive principal components, and domain vectors from the metrics.  Use domain metrics in the analysis

Multiple discriminant analysis: Quality model  Two aspects:  Classification errors:  Consequences of classification errors:

Multiple discriminant analysis  Uncertainty: