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研究生： 邱南星
Nan-Hsing Chiu
論文名稱： 類比式軟體成本預估模式正確性改善之研究
A Study of Accuracy Improvement on Analogy-Based Software Cost Estimation Model
指導教授： 黃世禎
Sun-Jen Huang
口試委員: 朱治平
Chih-Ping Chu
李允中
Jonathan Lee
林我聰
Woo-Tsong Lin
李漢銘
Hahn-Ming Lee
徐俊傑
Chiun-Chieh Hsu
李國光
Gwo-Guang Lee
學位類別： 博士
Doctor
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2006
畢業學年度： 95
語文別： 英文
論文頁數： 96
中文關鍵詞： 軟體成本預估 、類比式預估 、相似度度量 、基因演算法 、軟體專案管理 、軟體度量與分析
外文關鍵詞： Software Cost Estimation, Analogy-Based Estimation, Similarity Measure, Genetic Algorithm, Software Project Management, Measurement and Analysis
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    • 對於軟體產業與學術界而言，能夠可靠與正確地預估軟體開發或維護成本，一直是一個嚴峻的挑戰。類比式模式（Analogy-based Model）是一種廣泛使用的軟體成本預估（Software Cost Estimation）方法，專案相似度的度量（Similarity Measure）在類比式軟體成本預估模式中，扮演著相當重要的角色。然而，文獻中很少的軟體成本預估模式，探討到如何決定在類比相似度度量中每個軟體成本因子（Cost Driver）適宜的權重，並且論述如何基於相似度度量之距離差，來調整相似歷史專案之成本以推導出成本預估值。
    本篇論文的主要目標在類比式軟體成本預估模式正確性改善之研究。本研究提出了加權式類比軟體成本預估調整模式（Weighted Analogy-based Software Cost Estimation Adjustment Model），採用基因演算法（Genetic Algorithm）來決定類比式成本預估模式中相似度度量之成本因子適宜的權重，並基於相似度度量所計算出的距離差，來推導出預估專案軟體開發成本的預估值，並分別採用兩個歷史軟體專案資料集來驗證本研究所提出之模式在軟體成本預估值正確性提昇的能力。
    實驗結果呈現出採用基因演算法在類比式軟體成本預估模式來決定相似度度量之成本因子適宜的權重，或者是利用基因演算法來決定軟體預估成本的修正量，皆是提昇軟體成本預估模式正確性的可行方式。本研究所提出之加權式類比軟體成本預估調整模式的正確性優於統計廻歸、類神經網路或決策樹之軟體成本預估模式，並且也比文獻中所提出之加權或調整式類比軟體成本預估模式有較高之軟體成本預估模式正確性的改善率。

    A reliable and accurate estimate of software development or maintenance cost has always been a challenge for both the software industrial and academic communities. Analogy-based method is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of cost drivers play a central role in analogy-based software cost estimation model. However, hardly any research work in the literatures has addressed the issue on how to determine the suitable weights of software cost drivers in the similarity measures of analogy-based software cost estimation model. And similarity, little theoretical or experimental works have been reported on the method of deriving a cost estimate from the adjustment of the reused cost based on the similarity distance.
    This dissertation aims to improve the estimation accuracy for the analogy-based software cost estimation model. A “weighted analogy-based software cost estimation adjustment model” is proposed in this dissertation. The proposed model utilizes the genetic algorithm (GA) method to determine the appropriate weights of cost drivers in the similarity measures of analogy-based software cost estimation model. In addition, the GA is also used to adjust the reused cost based on the similarity distances between pairs of projects. The approach of using two well-known software historical project datasets for verifying the proposed model is also illustrated in this dissertation.
    The experimental results show that applying GA to determine suitably weighted similarity measures of software cost drivers or applying a suitable linear model to adjust the reused cost is a feasible approach to improving the accuracy of analogy-based software cost estimation model. The proposed model is superior to regressions, neural nets or decision trees-based software cost estimation models. It also provides higher improvement rate on the accuracy of software cost estimates than those weighted or adjusted analogy-based software cost estimation model in the previous works.

    論文摘要 I Abstract II Acknowledgement III Table of Contents IV List of Figures VII List of Tables VIII Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivation 2 1.3 Research Scope 3 1.4 Outline of the Dissertation 4 Chapter 2 Overview of Analogy-Based Software Cost Estimation 7 2.1 Introduction 7 2.2 Analogy-Based Estimation 8 2.2.1 Similarity Measure 9 2.2.2 Weighted Analogy 10 2.2.3 Adjusted Analogy 13 2.3 Search Strategy 15 2.3.1 Search Technique 15 2.3.2 Genetic Algorithm 17 2.4 Discussion 22 Chapter 3 Optimization of Weighted Analogy 25 3.1 Introduction 25 3.2 Equally Weighted Analogy 26 3.3 Differently Weighted Analogy 27 3.3.1 Unequally Weighted Analogy 29 3.3.2 Linearly Weighted Analogy 29 3.3.3 Nonlinearly Weighted Analogy 29 3.4 Adaptation and Evaluation 30 3.4.1 Project Adaptation 30 3.4.2 Evaluation Criteria 31 3.5 Data Set Description 32 3.6 Experiment Results 35 3.6.1 The ISBSG Data Set 36 3.6.2 The DPS Data Set 43 3.7 Discussion 49 Chapter 4 Cost Adjustments for Analogy 53 4.1 Introduction 53 4.2 Unadjusted Analogy 54 4.2.1 Analogy with Euclidean Distance Metric 54 4.2.2 Analogy with Manhattan Distance Metric 55 4.2.3 Analogy with Minkowski Distance Metric 55 4.3 Adjusted Analogy 55 4.3.1 Adjusted Analogy with Euclidean Distance Metric 58 4.3.2 Adjusted Analogy with Manhattan Distance Metric 59 4.3.3 Adjusted Analogy with Minkowski Distance Metric 60 4.4 Evaluation Criteria 60 4.5 Data Set Description 61 4.6 Experiment Results 63 4.6.1 The DPS Data Set 64 4.6.2 The CF Data Set 69 4.7 Discussion 74 Chapter 5 Conclusion of Improving Analogies 79 5.1 Summary 79 5.2 Discussion 81 5.3 Future Work 82 Reference 85 Publication List 93 Curriculum Vitae 95

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