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基于Boosting的集成k-NN软件缺陷预测方法

, PP. 792-802

Keywords: 软件缺陷预测,k-近邻(k-NN),软件度量元,集成学习

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Abstract:

软件缺陷预测是改善软件开发质量,提高测试效率的重要途径。文中提出一种基于软件度量元的集成k-NN软件缺陷预测方法。首先,该方法在不同的Bootstrap抽样数据集上迭代训练生成一个基本k-NN预测器集合。然后,这些基本预测器分别对软件模块进行独立预测,各基本预测值将被融合生成最终的预测结果。为判别新的软件模块是否为缺陷模块,设计分类阈值的自适应学习方法。集成预测结果大于该阈值的模块将被识别为缺陷模块,反之则为正常模块。NASAMDP及PROMISEAR标准软件缺陷数据集上的实验结果表明集成k-NN缺陷预测的性能较之广泛采用的对比缺陷预测方法有较明显的提高,同时也证明软件度量元在缺陷预测中的有效性。

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