在實務上找出確切之軟體可靠度對於軟體研發產業極為重要，因為軟體可靠度可作為研發與檢測的工作人員決策之依據，使他們能在檢測期中更有效率的改善軟體系統。基於過去之經驗，實際觀察之累積錯誤數量常與S型或指數型之均值函數有關，因此許多軟體可靠度模型會利用非齊次普瓦松過程描述軟體失效現象。然而完美除錯之假設，即一旦系統內錯誤被偵測，將馬上被移除並更正之，在實務上較不合理。為了提供軟體研發者更為準確的資訊，並且幫助軟體研發及檢測團隊能更直接掌握軟體失效過程以及軟體可靠度，同時使軟體系統的穩定性與軟體檢測所需之成本達成平衡，本研究乃以建構一般化之軟體可靠度成長模型之目的，利用非齊次普瓦松過程合理以描述時間變動之累積錯誤偵測數量，並且考量除錯過程中不完美除錯之可能性。本研究建構之模型，同時考量學習效果與測試努力程度，以期能夠在不同的失效資料集中，取得較佳之配適度。本研究並利用最小平方估計法，將各模型參數估計出，並且帶入各錯誤資料集，確定本模型之配適良好，接著與其他文獻模型比較，本模型表現也較佳。最後考量軟體檢測期之檢測成本，探討成本可能的情境，並且利用實例分析，提供檢測者恰當的軟體最佳釋出時間之建議。

For the last two decades, the development of computer software systems has become a critical issue in business. Based on past experiences, most of the software reliability growth models are constructed by Non-Homogeneous Poisson Process (NHPP), and S-shaped and exponential-shaped types of behavior are usually assumed. However, such models often assumed perfect-debugging which may be suitable only for restricted cases, and thus narrow the scope of application. Perfect-debugging, which assumes that an error can be immediately removed once being detected, it is unrealistic in practice. In this study, both imperfect-debugging and learning effect are considered in describing the failure process of the software reliability growth model. In order to provide the software developer with more information and assist the testing team in managing the software failure process to achieve a balance between more system stability and less testing costs, this study constructs a general software reliability growth model using NHPP and takes the possibility of imperfect-debugging into consideration. The proposed model considers both learning effect and testing effort to provide better usefulness than other models in fitting with different failure data sets. In addition, a numerical case is given to verify the effectiveness of the proposed model and a optimal release time of the software to the market is provided for the software developer.

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