关键词: 软件质量保障, 软件缺陷数预测, 特征选择, 实证研究

Abstract: Previous works in software defect prediction mainly focused on classifying software modules as defect-prone or not. How to quantify the number of defects in a software module has rarely been investigated. To address such issue, the paper proposes a two-stage feature selection approach for software defect number prediction FSDNP：feature clustering phase and feature selection phase. The feature clustering phase clusters highly correlated features using a density-based clustering method, and the feature selection phase removes irrelevant and redundant features from each cluster using three heuristic ranking strategies. FSDNP compares with six state-of-the-art baseline approaches using average absolute error and average relative error on PROMISE dataset. The results show FSDNP can effectively remove irrelevant and redundant features and build effective software defect number prediction model.

Key words: software quality assurance, software defect number prediction, feature selection, empirical studies