Research Of Some Key Issues In Software Quality Prediction Model

With the rapid development of software industry, software products become more and more complicated, and developing processes become more and more difficult to control. Moreover, software failures always lead to great loss in practice. In such circumstance, the software quality prediction system is proposed to identify the fault-prone modules in the early phase of software development life cycle, so that resources can be correctly allocated in the following development and testing. This approach has been proved to be able to effectively shorten development cycle, reduce maintenance cost and highly improve software quality. This dissertation studies some key issues in software quality prediction modeling, including feature selection, classifiers combination and rules extraction. It is based on three large telecommunication softwares developed by Lucent Technology Optical Networks Ltd.Firstly, a frame of software quality prediction is proposed in this dissertation. This frame consists of three parts: the former, kernel and the latter. In the former stage, the main task of software quality prediction is feature selection and data preprocessing. The data set having been selected and preprocessed is divided to training set and testing set. In the kenel stage, software quality model is trained with specific traing algorithm based on training set and the trained model is tested over testing set. The results of kenel stage include the description and prediction result of trained model. The main task of latter stage is to extract rules from trained model and evaluate its performance.Secondly, a clustering and feature selection approach based on genetic algorithm (CFGA) is introduced in the former stage. The clustering and feature selection are performed in the same evolution phase. The fitness function of feature selection is described by clustering results. Some parameters in the fitness function can be adjusted to make the clusters loose or compact. This approach can work in both supervised learning and unsupervised learning. For unsupervised learning, the results of clustering will be further analyzed by software expert. The clustering and feature selection can significantly reduce the workload of expert. While for supervised learning, the clusters will be classified to outlier, high purity clusters and low purity clusters. The outliers...