臺灣博碩士論文加值系統

目前大部分軟體成本的預估，是以軟體的特徵、大小或功能點為主。但是其中個別項目的權重會直接影響到預估的準確度。本篇論文提出GAF來預估軟體成本。GAF結合了基因演算法（Genetic Algorithm，GA）與模糊理論（Fuzzy Theory）兩種演算法。透過GA找尋最佳化的特性，找出適當的成本動因。再將其與人月（Person Months，PM）求得平均值。這個平均值可作為Fuzzy membership function的輸入參數。透過Fuzzy的規則，進一步找出最佳的PM值。強度關聯誤差(Magnitude Relative Error，MRE)與平均強度關聯誤差（Mean Magnitude Relative Error，MMRE）用來表示評估軟體方法的準確度。實驗結果顯示: Fuzzy方法去評估軟體的MMRE為20.99%，而GAF的MMRE為9.38%。這證明在軟體成本預估方面GAF比Fuzzy方法更準確。

At present, most software cost estimates, based on the software''s features, size or functional point. However, the weights of individual items directly affect the forecast accuracy. This paper presents the GAF method to estimate software costs. The GAF method combines genetic algorithm (GA) and fuzzy theory algorithms. The appropriate are optimized by GA. Then the average values of those weighting coefficients and the values of Person Months (PM) are computed. These average values can be used as input parameters of Fuzzy membership function. By Fuzzy rules, the predicted PM will be found. The Magnitude Relative Error (MRE) and the Mean Magnitude Relative Error (MMRE) are used to represent the accuracy of the software assessment. The experimental results show that: the MMRE of Fuzzy approach is 20.99%, while the GAF''s MMRE is 9.38%. This proves that GAF is more accurate than Fuzzy for software cost estimation.

摘要 I
ABSTRACT II
目錄 III
表目錄 IV
圖目錄 V
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 重要性與貢獻 3
第四節 論文架構 4
第貳章 文獻探討 5
第一節 認知科學 5
第二節 統計分析 7
第三節 軟體可靠度模型 8
2.3.1 曲線擬合模型 8
2.3.2 植入錯誤模型 9
2.3.3 失效率模型 10
2.3.4 異質性卜瓦松過程模型 10
2.3.5 馬可夫結構模型 10
2.3.6 可靠度成熟模型 11
第參章 研究方法 13
第一節 COCOMO方法 13
第二節 基因演算法 15
第三節 模糊理論 17
第四節 實驗設計 18
第五節 實驗流程 20
第肆章 實驗結果與分析 22
第一節 資料說明 22
第二節 實驗結果 22
第伍章 結論與未來研究 29
參考文獻 30
附錄A 34
附錄B 50

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