Defect prevention in software processes: An action-based approach

Accurately predicting actions that cause many defects by mining records of performed actions is a challenging task due to the rarity of such actions. To address this problem, the under-sampling is applied to the data set to increase the precision of predictions for subsequence actions. To demonstrate the efficiency of this approach, it is applied to a business project, revealing that under-sampling with FSS successfully predicts the problematic actions during project execution. The main advantage utilizing ABDP is that the actions likely to produce defects can be predicted prior to their execution. The detected actions not only provide the information to avoid possible defects, but also facilitate the software process improvement.