摘要
軟體可靠度為軟體品質之重要衡量指標亦為研發與檢測團隊做相關決策之依據，然而一般軟體可靠度成長模型基本假設之一為完美除錯，但實際除錯過程卻可能是因移除一錯誤而產生新的錯誤或減少其他錯誤，或是檢測團隊無法立即將該錯誤完全移除，此即不完美除錯之現象；此外，檢測團隊於軟體偵測與移除過程會產生學習效果，而學習效果為反覆操作相同動作時產生，但軟體中的錯誤並非完全相同，可依複雜程度的不同進行區分，因此學習效果與錯誤分類將影響系統累積錯誤偵測數量。實務上，軟體檢測期間的環境可能產生改變，如聘請新的檢測人員、更換硬體設施、改變檢測工具或策略等，此改變之時點即轉折點。本研究同時考量錯誤移除過程中的人為因素與錯誤之本質，將不完美除錯、學習效果、錯誤分類及轉折點加入以非齊次普瓦松過程建構之軟體可靠度成長模型，其中不完美除錯以錯誤增減率與除錯效率作為考量除錯過程中額外的錯誤增減情形與錯誤無法完全移除之情形，目的是希望提供一考量完善且貼近實務之模型，以提供軟體研發與檢測者準確的軟體可靠度資訊，並進一步作為相關決策之依據。本研究以最小平方法對所蒐集之錯誤偵測資料估計參數，確定本模型之配適良好，並與其他文獻模型比較，本模型也表現較佳。最後考量軟體檢測期間之檢測成本，以達到可接受之軟體可靠度水準為限制，利用實例分析，提供檢測者軟體最佳可以上市時間之建議，並針對成本函式進行敏感度分析，探討各參數對決策變數與最低總成本之影響。

Abstract
Software reliability is an essential quality attribute which can provide useful information for software developers. Most software reliability growth models are constructed based on the assumption of perfect debugging which assumes that an error can be immediately removed when it is detected and would not cause any new error in the software. However, software debugging is usually imperfect in practice. As the software testing process proceeds, the learning effect gradually emerges when the testing staff performs more error detections and removals. Moreover, errors can be categorized by their complexity. In practice, the testing environment may not be homogeneous due to the change of testing personnel, tools, and strategies. The time at which the environment changes is called the change point. This study constructs a software reliability growth model based on NHPP which considers imperfect debugging, learning effect, types of errors, and a change point to manage the software testing process for software developers. The proposed model is evaluated using several sets of software failure data, and the results show that it has a better fit than that of some other selected models. Numeral examples and the sensitivity analysis are performed, and an optimal software release policy is also suggested.

參考文獻
中文部分
黃翊绮，「考慮不完美除錯之軟體可靠度成長模型」，國立成功大學工業與資訊管理研究所碩士論文，民100。

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