本論文運用類神經網路(Neural Network)方法於軟體可靠度評估的研究。現有軟體可靠度分析都有著一些限制與假設，而這些限制與假設都難以去確認與驗證。另一方面，由於類神經網路運作方式類似於人類大腦思考的方式，因此他可以學習不同的軟體開發專案的錯誤歷史資料來作可靠度評估與預測，這就是類神經網路這幾年來逐漸被應用在軟體可靠度評估的原因。在這本論文中，我們首先呈現如何應用類神經網路的方法於軟體可靠度成長模型，我們嘗試著將類神經網路導成數學式子，將其與現有的模型作比較，探討它們之間的關聯。在導出它們之間的關聯後，接下來，我們示範該如何應用類神經網路方法於傳統的參數模型上。我們也更進一步的展示如何應用類神經網路的方法來混合模型，由於類神經網路有函數合成的特性，因此我們將其應用在現有的軟體可靠度模型的合成。最後，我們採用真實軟體錯誤的歷史資料來評估我們所提方法的性能。我們從三個方面來評估性能：相似性程度(Goodness of Fit)，短期預測(Short-term Prediction)，和長期預測(Long-term Prediction)。從實驗數據結果，一再顯示出類神經網路模型比傳統的模型有著更好的性能。

This thesis presents the results of an investigation of the application of neural network to software reliability assessment. Recently neural networks have been applied for software reliability because of its characteristic which can perform as human brains. But in general, software reliability analysis requires specifications of parametric distributions and certain assumptions that are difficult to validate at times. In this thesis, we first present the methodology of the neural network on the software reliability growth model. We try to derive the neural network approach into mathematics expressions while most researchers think that neural network is a black-box method. Furthermore we compare the neural network model with the conventional parametric models. We also demonstrate how to apply the neural network method on the traditional parametric models. Furthermore, we show how to make the models efficiently by using neural networks to achieve combinational models. At last, experimental examples using the real software reliability failure data sets are given to evaluate the performances of the proposed model. We compare the performances of models from three aspects: goodness-of-fit, short-term predictions, and long-term predictions. From the numerical results, we can conclude that the neural network model has better performances than the traditional models.

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