计算机科学 ›› 2017, Vol. 44 ›› Issue (12): 131-134.doi: 10.11896/j.issn.1002-137X.2017.12.026
王铁建,吴飞,荆晓远
WANG Tie-jian, WU Fei and JING Xiao-yuan
摘要: 提出一种多核字典学习方法,用以对软件模块是否存在缺陷进行预测。用于软件缺陷预测的历史数据具有结构复杂、类不平衡的特点,用多个核函数构成的合成核将这些数据映射到一个高维特征空间,通过对多核字典基的选择,得到一个类别平衡的多核字典,用以对新的软件模块进行分类和预测,并判定其中是否存在缺陷。在NASA MDP数据集上的实验表明,与其他软件缺陷预测方法相比,多核字典学习方法能够针对软件缺陷历史数据结构复杂、类不平衡的特点,较好地解决软件缺陷预测问题。
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