A Novel Method for Early Software Quality Prediction Based on Support Vector Machine

Abstract

The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting support vector machine (SVM) in the classification of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It is well known that SVM generalizes well even in high dimensional spaces under small training sample conditions. We consequently propose a SVM-based software classification model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Experimental results with a medical imaging system software metrics data show that our SVM prediction model achieves better software quality prediction than some commonly used software quality prediction models.