During the planning phase of a new project, a challenge faced by software project managers is to predict software development effort and cost. The allocation of appropriate resources and the planning of reasonable schedules based on reliable estimations of effort then become necessary. Grey Relational Analysis (GRA) has been extensively used in the field of software effort estimation. However, we found that most of traditional GRA methods only consider non-weighted similarity measure for predicting software development effort. Therefore, this dissertation firstly proposes non-weighted, distance-based, correlative, linear, nonlinear, and maximal weights to be integrated into GRA for software effort estimation. The experimental results indicate that the weighted GRA performs better precision than the non-weighted GRA. The results also demonstrate that the weighted GRA can reduce the variance of predictions over other estimation techniques.
Software reliability analysis over testing and maintenance phases is performed by measuring the software quality before shipping a product. We have noticed that one of the most important parameters controlling software reliability growth is the fault reduction factor (FRF) proposed by Musa. During the software testing process, FRF could be influenced by many environmental factors, such as imperfect debugging, debugging time lag, etc. Thus, in this dissertation, we consider FRF to be a time-variable function and further study how to integrate time-variable FRF into software reliability growth modeling. Some experimental results show that the proposed models can improve the accuracy of software reliability estimation. Finally, the analytic results indicate that adjusting the value of FRF may affect the release time as well as the development cost.
For modular software systems, traditional software reliability methods are insufficient to analyze inter-component interactions of modular software systems. We propose an adaptive framework of incorporating path testing into reliability estimation for modular software systems. Three estimated methods based on common program structures, namely, sequence, branch, and loop structures, are proposed to calculate the path reliability. Consequently, the derived path reliabilities can be applied to the estimates of software reliability. Some experiments are performed based on two real systems and sensitivity analysis. Experimental results show that the path reliability has a high correlation to the actual software reliability. For software with loop structures, a smaller loop number can be assigned to derive an acceptable estimation of path reliability. Further, the sensitivity analysis can be used to identify critical modules and paths for resource allocation.

在軟體開發過程中，如何精確的預估軟體開發所需的工作量與成本往往是專案管理者面臨的最大挑戰；事實上，軟體開發工作量的預估為專案管理中一門重要的課題，亦如專案的人力資源分配與時程管理，都仰賴目前評估的軟體開發工作量度量值。過去幾年間，灰關聯分析方法(Grey Relational Analysis, GRA)已被廣泛應用於軟體開發工作量預估領域中，然而我們進而發現傳統的灰關聯分析方法只考慮未加權式相似度量(non-weighted similarity measure)，因而容易造成不精確的預估結果。因此，本論文考量加權式相似度量，包含距離基準(distance-based)、關聯式(correlative)、線性關聯式(linear)、非線性關聯式(nonlinear)、與最大值基準(maximal)的加權式灰關聯分析方法；實驗的結果顯示，加權式灰關聯分析方法相較於未加權式灰關聯分析方法與其他預估方法擁有較精確的軟體開發工作量預估值，並且加權式灰關聯分析方法可以有效減少預估值的變異數。
軟體可靠度分析是一門基於量測軟體品質的科學研究，在軟體測試與維護過程之中，專案管理者可以採用軟體可靠度成長模型(Software Reliability Growth Model, SRGM)了解軟體錯誤行為發生與測試資源投入狀況，模型之中一個影響軟體可靠度成長的重要參數為錯誤減少因子(fault reduction factor, FRF)，根據學者Musa的定義，錯誤減少因子為錯誤減少數(net fault reduction)與失效數(failures experienced)的比率，我們在軟體測試過程中發現錯誤減少因子容易受到許多測試因素影響，例如：不完美除錯(imperfect debugging)與除錯時間延遲(debugging time lag)等。因此，在本論文中，我們藉由分析許多軟體測試資料，進而提出整合時間變數的錯誤減少因子(time-variable FRF)於軟體可靠度成長模型之中；實驗結果顯示所提出的方法可以提昇軟體可靠度預估的精確度，並且敏感度分析顯示調整錯誤減少因子確實會影響釋放時程與開發成本。
於模組化軟體系統之中，由於往往所需的測試個案(test case)數量過多，傳統可靠度方法並不能有效分析模組間複雜地錯誤傳遞過程。因此，我們提出利用路徑測試(path testing)與可靠度評估於模組化軟體系統，基於序列式(sequence)、分支式(branch)、與廻圈(loop)式系統架構，我們提出的方法可適應(adaptive)於模組化軟體系統可靠度評估，進而在軟體開發早期分析路徑可靠度(path reliability)；實驗過程採用二組系統真實個案研究與敏感度分析，結果顯示路徑可靠度和整體系統可靠度有著高度關聯性，若系統存在廻圈(loop)式系統架構，所提出的方法只需少量廻圈數即可得到準確的評估結果，此外，敏感度分析結果可用於找出系統架構中關鍵模組與路徑(critical module and path)，並且幫助專案管理者做資源分配。

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