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研究生:林俊宇
研究生(外文):Jun-Yu Lin
論文名稱:應用迴歸分析方法預測軟體發展時程
論文名稱(外文):Predicting software development schedule using regression analysis
指導教授:朱治平朱治平引用關係
指導教授(外文):Chih-Ping Chu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:68
中文關鍵詞:預測軟體工程時程模型
外文關鍵詞:predictingpredictionestimatingestimationschedule modelsoftware engineering
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  在軟體工程的領域中,如何去估算軟體發展的成本與時程是最困難但也是最重要的一部份。本文旨在提供一個如何利用迴歸分析方法來預測軟體發展時程的指引,並以軟體發展時程發生變異原因的角度為出發點,界定軟體時程發生變異的共同影響因子,並以這些影響因子來建立與解釋軟體發展時程模型,進而藉以預測軟體發展時程。

  首先,我們利用魚骨圖與變異數分析方法界定了軟體發展時程變異發生的共同影響因子,並以簡單迴歸分析方法建立各個因子的軟體發展時程模型。接著,我們利用了複迴歸分析方法建立了多重因子的軟體發展時程模型,並且說明了預測區間的計算方法。最後,我們利用了逐步迴歸分析演算法來精緻化所建立的軟體發展時程模型,排除了有預測能力但重疊性高的因子。除此之外,我們亦介紹了非線性的軟體發展時程模型的建立方法。
  In software engineering it is very difficult but critical to forecast how much effort and time it will take to develop a system. The objective of this thesis is to provide a guideline to estimating the software development schedule when using the regression analysis techniques. The common causes of schedule variation will be identified first, and then be employed to construct and explain the software development schedule model.

  We first use fishbone diagram and ANOVA approaches to identifying the common causes of schedule variation, and then establish the software development models in association with each individual factor. Next, we establish multiple-factor software development schedule model and give an example to compute the prediction interval. Lastly, we use the stepwise regression algorithm to refine the software development schedule model, i.e, removing the factor with prediction power but without contribution to the model. In addition, we also introduce nonlinear software development schedule model.
Table of Contents iv
List of Tables vi
List of Figures vii
Chapter 1 Introduction - 1 -

1.1 Motivation - 1 -
1.2 Organization - 3 -

Chapter 2 Common Estimation Techniques - 4 -

2.1 Expert Judgment - 4 -
2.2 COCOMO - 5 -
2.3 Function Points Analysis - 9 -

Chapter 3 Software Development Schedule Model - 11 -

3.1 Dependent Variable - 11 -
3.2 Independent Variables - 12 -
3.2.1 Total Number of Requirements - 12 -
3.2.2 Requirements Complexity - 13 -
3.2.3 Total Number of Requirements Changes - 14 -
3.2.4 Staff’s domain knowledge and Experience - 14 -
3.2.5 Number of Personnel - 14 -
3.2.6 Use of Tools - 14 -
3.2.7 Number of Methods Use - 15 -
3.2.8 Customer Participation - 16 -
3.3 Cause and Effect Analysis - 17 -
3.4 Validation of the Independent Variables - 18 -
3.4.1 Simple Linear Regression - 19 -
3.4.2 Analysis of Variance (ANOVA) - 22 -
3.4.3 Results of Data Set - 24 -
3.5 Assessment of the Schedule Model - 26 -
3.5.1 Evaluation Criteria - 26 -
3.5.2 Results of Schedule Model Assessment - 27 -
3.6 Application of Multiple Linear Regression - 27 -
3.6.1 Multiple Linear Regression - 28 -
3.6.2 Multiple Linear Regression Model - 31 -
3.7 Prediction Interval - 33 -
3.7.1 Interval Estimation Method - 33 -
3.7.2 Example of Prediction Interval - 35 -

Chapter 4 Application of Stepwise Regression for Model Refinement - 37 -

4.1 Forward Stepwise Regression - 38 -
4.1.1 Example of Forward Stepwise Regression - 40 -
4.2 Backward Stepwise Regression - 44 -
4.2.1 Example of Backward Stepwise Regression - 45 -
4.3 Stepwise Regression - 47 -
4.3.1 Example of Stepwise Regression - 48 -

Chapter 5 Application of Nonlinear Regression - 54 -

5.1 Variable Transformation - 54 -
5.2 Example of Nonlinear Schedule Model - 56 -

Chapter 6 Conclusions and Discussion - 58 -

References - 61 -

Appendix A Data Generation - 65 -

Appendix B Process Performance Modeling Tool - 67 -
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