Design and Development of Software Fault Prediction Model to Enhance the Software Quality Level

Software fault prediction models play an important role in software quality assurance. They identify software subsystems (modules, components, classes, or files) which are likely to contain faults. These subsystems, in turn, receive additional resources for verification and validation activities. Fault prediction models are binary classifiers typically developed using one of the supervised learning techniques from either a subset of the fault data from the current project or from a similar past project. In practice, it is critical that such models provide a reliable prediction performance on the data not used in training. Variance is an important reliability indicator of software fault prediction models. Models were built using both, original software metrics (RAW) and their principle components (PCA). Two-way ANOVA randomizedcomplete block design models with two blocking variables are designed with average absolute and average relative errors as response variables. System release and the model type (RAW or PCA) form the blocking variables and the prediction technique is treated as a factor. Using multiple-pairwise comparisons, the performance order of prediction models is determined. We observe that for both average absolute and average relative errors, the CARTLAD model performs the best while the S-PLUS model is ranked sixth. Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility of constructing simpler networks with fewer nodes and arcs remain unexplored.