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研究生： 黃培倫
Huang, Pei-lun
論文名稱： 準確評估軟體成本之研究
Accurately Estimate Software Development Cost
指導教授： 朱治平
Chu, Chih-Ping
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2007
畢業學年度： 95
語文別： 英文
論文頁數： 59
中文關鍵詞： 軟體規模 、軟體成本 、COCOMO模式 、軟體成本估算修正模式
外文關鍵詞： software size, software cost estimation model, Software cost, COCOMO model
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    • 在政府大力提倡導入CMMI能力成熟度整合模型下，台灣軟體業界已經開始注意軟體工程的重要性。而在軟體工程中關於成本／工作量的預測是一項重點課題。工作量預測的準確性，會對專案的工作安排、資源配置有重要的影響。由於目前台灣尚未建立公認可接受之成本分析模型，本論文嘗試提出建立此一成本分析模型及其流程，以提供台灣業界參考。

    在建立成本分析模型之後，需考慮到如何改善成本分析模型的正確性。一個組織在不斷的進行新的軟體開發，會累積一定數量的評估資料，而這些評估資料如未加以利用於未來新的專案的評估，是一種資源的浪費。本論文提出以資料探勘的方法，從過去的評估資料中擷取法則，以協助新的軟體專案發展工作量的估算。由於目前台灣軟體產業在估算方面的數據保存習慣尚未養成，因此無法採集到足夠的數據以為分析，但本文就NASA所提供的資料來進行實驗，初步實驗結果顯示資料探勘可獲得不錯的改進成果。因此未來台灣的軟體產業界也應保留成本資料以供其應用資料探勘技術建立更精確的成本預估模型。

    Taiwan government is strongly promoting CMMI (Capability Maturity Model Integration), this lately causes Taiwan software industry fields pay special attention to the significance of software engineering. In software engineering research area, cost/effort estimation is one of the most important issues. Effort estimation accuracy will affect the availability of resource allocation and task scheduling. Because Taiwan software industries have not created their own cost estimation models yet, this thesis proposes a preliminary software cost estimation model and the process of establishing a cost model.

    After the cost model has been established, how to make it more accurate is another issue to face. An organization that develops new software products will gernerally generate lots of metrics data. But, if these data are not used for future project, that is a resource waste. This thesis also presents a data mining method, mining historical cost data to assist the software development effort estimation. This thesis use NASA cost estimation data to experiment with the effect of data mining, and the result is satisfied.

    摘要 i Abstract ii 誌謝 iii Table of Contents iv List of Tables vii List of Figures ix 1. Introduction 1 1.1. Motivation 1 1.2. Organization 3 2. Background and Literature Survey 4 2.1. Common Estimation Techniques 4 2.2. Software Sizing Methods 6 2.3. Cost Model 8 2.3.1. COCOMO 8 2.3.2. Use Case Point (UCP) 9 2.4. Cost Model Performance Measures 10 2.5. Data Mining: Association Analysis 12 2.6. Validation Methods 13 3. Preliminary Study of Accurately Estimating Software Development Cost 15 3.1. Process 15 3.1.1. Select Sizing 16 3.1.2. Software Measurement 17 3.1.3. Statistic analysis 18 3.1.4. Survey cost drivers 19 3.1.5. Mining Historical Data to Improve cost estimation 21 3.2. Case Study 24 3.2.1. Company A 24 3.2.2. Company B 26 3.3. Calibrating the Model 26 4. Mining Historical Data to Improve Effort Estimation Accuracy 28 4.1. Assumption 28 4.2. Data Analysis 29 4.2.1. Data Sets 29 4.2.2. Classification with Prediction Accuracy 32 4.2.3. Mining Association Rule 34 4.3. Validation 36 4.3.1. Validation with Original Data 36 4.3.2. Validation by Holdout Validation 37 4.3.3. Validation by Leave-one-out Cross Validation 37 4.3.4. Validation with Random Generated Data 38 4.4. System Architecture 39 5. Results and Discussion 40 5.1. Accuracy of the Mining Repository 40 5.2. Rule Pruning 41 5.3. Multiple Rule 42 5.4. Combine with other Calibration Methods 44 6. Conclusions and Suggestions 46 6.1. Conclusions 46 6.2. Future Work 47 6.3. Suggestions 49 APPENDIX A: Sample of NASA93 50 APPENDIX B: COCOMO 81 numeric values 51 APPENDIX C: Use Case Point numeric values 52 Reference: 56

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