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。

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However, the errors are usually relatively high, even leading to unreliable results, because many approximations and assumptions were involved in J-O model.

由于J - O模型涉及许多近似和假设，计算结果误差较大，可能导致结果不可靠。

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Based on G-O model, we propose a software cost model incorporating fault-remove time which followed exponential distribution and reliability function of software in this paper.

传统的软件费用模型大多假设排错时间忽略不计，而且很少考虑到软件的可靠性指标，影响了模型应用的范围和精度。

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