基于机器学习的软件缺陷预测是一种有效的提高软件可靠性的方法。
Software Quality Prediction Based on Machine Learning is an effective way to improve software reliability.
目前软件缺陷预测技术已经被证明是提高软件可靠性和软件质量的有效方法。
Now software faults prediction is a proven technology to improve software quality and reliability.
为了确切地估计软件缺陷分布，提出了基于AODE和再抽样的软件缺陷预测模型。
To evaluate software defect distribution exactly, a software defect prediction model based on AODE and resampling is put forward.
为了降低开发成本，在有限的资源限制下更有效地提高软件产品的质量，软件工程领域开始了对软件缺陷预测的研究。
Under the limited resources, in order to reduce the cost and improve the products' quality more effectively, the research of software defect prediction in software engineering was beginning.
结果表明：神经网络预测软件缺陷数比传统的G - O模型有更好的质量拟合度和估测能力。
It indicates that the neural network can produce models with better fitting quality and predictive quality in comparison with traditional G-O model.
结果表明：神经网络预测软件缺陷数比传统的G - O模型有更好的质量拟合度和估测能力。
It indicates that the neural network can produce models with better fitting quality and predictive quality in comparison with traditional G-O model.
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