Paper Title
Model for Software Testing and Quality Assessment using Classification Approach
Abstract
Defect in software systems continue to be a major problem. High quality of software is ensured by Software
reliability and Software quality assurance. A software defect causes software failure in an executable product. A variety of
software fault predictions techniques have been proposed, but none has proven to be consistently accurate. The objective in
the construction of models of software error prediction is to use measures that may be obtained relatively early in the
software development life cycle to provide reasonable initial estimates of quality of an evolving software system. Here
various data mining classification and prediction techniques viz. Neural Network (NN), Decision tree (DT), Support Vector
Machine (SVM),K-Nearest Neighbour(k-NN) and Naïve Bayes (NB) have beenanalysed and compared for software defect
prediction model development.. Further, stacking, bagging and boosting, a meta modelling techniques in order to enhance
the accuracy of the classification techniques have also been used. For this DATATRIEVETM project carried out at Digital
Engineering, Italy has been used to validate the algorithm. The results showed that model using NN classification technique
was a better prediction model.
Keywords - Software Defect, DT, NN, kNN, Naive Bayes, Classification techniques, Data Mining