Multi-attribute dependent bug severity and fix time prediction modeling

A software bug is characterized by many features/attributes out of which some are entered during the time of bug reporting whereas others are entered during the bug fixing. Severity is an important bug attribute and critical factor in deciding how soon it needs to be fixed. During the initial period of bug reporting, its severity changes and get stabilizes over a period of time. Severity identification is a major task of triagers, whose success affects the bug fix time. The prediction of bug fix time will help in estimating the maintenance efforts and better software project management. We investigated the association among the bug attributes and built multi-attribute based classification and regression models for bug severity and fix time prediction. Bug severity and fix time prediction models have been built using the combinations of different independent bug attributes. We have used different classification and regression techniques, namely Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors (k-NN), Ordinal Regression (OR), Fuzzy Linear Regression (FLR), Fuzzy Multi Linear Regression (FMLR), Multiple Linear Regression (MLR), Support Vector Regression (SVR) and k-Nearest Neighbors Regression (k-NNR) to build the models. Our models are tested on the real world datasets from famous open source project: Mozilla. k-NN gives better performance than NB and SVM in terms of precision and f-measure for bug severity prediction. In terms of goodness of fit, SVR is better than MLR and k-NNR for bug fix time prediction. The proposed mechanism is able to predict severity and fix time for newly reported bugs. Empirical results reveal that the multi-attribute based classification and regression models work well for bug severity and fix time prediction. The two newly derived attributes Summary weight and Bug age are found to be good predictors of severity across all the used techniques. In case of bug fix time prediction, Bug age is found to be a good predictor.