在軟體測試階段，專案管理者需要精準的分配測試資源以滿足可接受的軟體品質並且減少測試成本。直到目前為止，有許多論文探討過測試成本預估的問題並且提出了許多模型，例如韋柏函數、瑞利函數和羅吉斯函數等等。在實務上，測試資源的消耗率深深受測試階段中一些無法預期的事件所影響。例如新測試人員的加入或是顧客要求產品提前完工。可惜的是，大部分的測試心力函數將測試資源消耗率視為常數。因此在此論文中，我們使用了變動點的概念改進帕爾曲線模型，並且預期此模型可以更有彈性的符合各種測試資源分配情形。另一方面，各種不同的測試資源分配深深影響著軟體可靠度的成長趨勢。因此，一個良好的測試心力函數可以提供充足的資源分配資訊來改善軟體可靠度成長模型的表現。在實驗中，我們以使用變動點的韋柏模型做為比較，並且檢驗軟體可靠度成長模型的效能，而這些軟體可靠度成長模型使用著我們提出的測試心力函數。為了確保實驗結果的正確性，我們使用了兩種參數估計方法，最小平方法以及最大概似估計法。最後我們使用提出的模型來討論最佳軟體釋放時間，並且基於可靠度以及測試成本這兩個限制來推導。

During software testing phase, the software manager has to accurately allocate testing resources to arrive at acceptable software quality and reduce the cost of testing. In the last twenty years, there have been many papers discussing the problem of testing-effort prediction, and also proposed many TEFs such as Weibull TEF, Rayleigh TEF and logistic TEF, etc. In practice, the testing effort consumption rate is strongly affected by unexpected events during the testing phase. For instance, the participation of new tester or the consumer advances the deadline, etc. However, most TEFs consider testing effort consumption rate as a constant value. Due to this reason, we proposed Parr curve TEF which incorporates the concept of multiple change points in the thesis, and expected the application of change points can make the Parr curve TEF more flexible to adapt to any situation of testing resource allocation. On the other hand, the testing resource allocation deeply influences the growth trend of software reliability. Thus a powerful TEF can provide sufficient testing-effort consumption information to enhance the capability of SRGMs. In the experiment, we compared to Weibull TEF with change points and examine the performance of SRGM with 1-CP Parr curve TEF. Besides, the 2-CP Parr curve is also implemented and induced in the experiment. In order to guarantee the correctness of experiment result, both parameter estimated methods are used, LSE & MLE. Finally, we discuss the policy of optimal release time based on the proposed SRGM. Dependent on constraints, reliability and testing cost, the optimal release time was induced.

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