Cost-sensitive boosting neural networks for software defect prediction

Software defect predictors which classify the software modules into defect-prone and not-defect-prone classes are effective tools to maintain the high quality of software products. The early prediction of defect-proneness of the modules can allow software developers to allocate the limited resources on those defect-prone modules such that high quality software can be produced on time and within budget. In the process of software defect prediction, the misclassification of defect-prone modules generally incurs much higher cost than the misclassification of not-defect-prone ones. Most of the previously developed predication models do not consider this cost issue. In this paper, three cost-sensitive boosting algorithms are studied to boost neural networks for software defect prediction. The first algorithm based on threshold-moving tries to move the classification threshold towards the not-fault-prone modules such that more fault-prone modules can be classified correctly. The other two weight-updating based algorithms incorporate the misclassification costs into the weight-update rule of boosting procedure such that the algorithms boost more weights on the samples associated with misclassified defect-prone modules. The performances of the three algorithms are evaluated by using four datasets from NASA projects in terms of a singular measure, the Normalized Expected Cost of Misclassification (NECM). The experimental results suggest that threshold-moving is the best choice to build cost-sensitive software defect prediction models with boosted neural networks among the three algorithms studied, especially for the datasets from projects developed by object-oriented language.