计算机工程与应用2019，Vol.55Issue(14)：61-68,8.DOI:10.3778/j.issn.1002-8331.1811-0103
FSDNP:针对软件缺陷数预测的特征选择方法
FSDNP:Feature Selection Method for Software Defect Number Prediction
摘要
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 redun-dant features and build effective software defect number prediction model.关键词
软件质量保障/软件缺陷数预测/特征选择/实证研究Key words
software quality assurance/software defect number prediction/feature selection/empirical studies分类
信息技术与安全科学引用本文复制引用
李叶飞,官国飞,葛崇慧,陈翔,倪超,钱柱中..FSDNP:针对软件缺陷数预测的特征选择方法[J].计算机工程与应用,2019,55(14):61-68,8.基金项目
国家自然科学基金(No.61472181) （No.61472181）
江苏省自然科学基金(No.BK20151392). （No.BK20151392）
