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研究生:韓文銘
研究生(外文):Wen-Ming Han
論文名稱:軟體專案時程、風險因子與績效互動影響之研究
論文名稱(外文):A Study on the Interactive Effects among Software Project Duration, Risk Factors and Project Performance
指導教授:黃世禎黃世禎引用關係
指導教授(外文):Sun-Jen Huang
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:103
中文關鍵詞:軟體風險管理風險元件風險暴露專案績效專案時程
外文關鍵詞:Software Risk ManagementRisk ExposureRisk ComponentProject PerformanceProject Duration
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根據Standish Group的軟體專案開發追蹤調查報告,儘管組織投入許多經費、時間與人力來開發軟體,軟體專案開發失敗的消息仍然時有所聞,這代表對許多組織而言,軟體專案開發是一項具有高度風險的工作。儘管如此,我們對風險因子的發生機率與衝擊程度的瞭解仍然十分有限,甚至缺乏瞭解風險因子與專案績效的相互關係以及專案時程與風險因子的關連。

本篇論文的主要目標在探討軟體專案時程、風險因子與績效間的相互關係。根據有系統的文獻回顧、建置軟體專案風險資料收集網站以及集群分析,本論文的主要二個貢獻如下:首先本研究彙整軟體風險管理領域於1991年至2006年間的文獻,並經由所收集之專案風險資料分析的結果,發現不同的軟體風險因子其發生機率與衝擊程度是有顯著差異的;此外藉由高績效專案、中績效專案與低績效專案的風險落差分析,透析軟體風險與專案績效的互動影響。最後,本研究也發現使用者風險、需求風險、規劃與控制風險以及團隊風險會受到軟體專案時程所影響,並透過風險元件來提供有效管理軟體風險的資訊。

根據上述所提到的研究發現,專案管理者可以採取適當的態度、技巧與作業實務來有效管理風險因子,而不是只單純告知專案管理者有哪些軟體風險因子需要注意而已。
Despite the fact that many organizations have invested a lot of money, time and effort to develop their software projects, the failures of software projects are still frequent based on the longitudinal analysis of the Standish Group. This stresses the fact that software projects pose various risks and daunting tasks for many organizations. However, currently we lack an understanding of the relative likelihood of occurrence and the various impacts of different software risks factors. And similarly, previous studies failed to analyze the gap between software risk factors and project performance and the invisible correlation of project duration to risk factors.

This dissertation aims to increase the understanding on the interactive effects of software project duration, risk factors and performance. Based on systematical literature review, web-based survey and clustering analysis, two contributions of this dissertation are summarize as below: Firstly, after summarizing the software risk management research work between 1991 and 2006 in the literature, this study analyizes the collected software risk management data and further finds that the likehihood of occurrence of software risks and composite impacts have significant differences on six risk dimensions. Moreover, it indicates that no association exists between the likelihood of occurrence and composite impact among the six risk dimensions. A pattern analysis of risks across high, medium, and low-performance software projects also shows the gap between software risks and project performance. Secondly, the study not only reveals that risk exposures associated with user, requirement, planning & control and team risk dimensions were affected by project duration, but also shows how to manage software risks effectively through observing trends in the risk components.

Based on the above-mentioned findings, project managers can accordingly adopt appropriate attitudes, skills, and practices to deal with risky areas more effectively rather than just identifying those software risks with which project managers should be concerned.
論文摘要 I
ABSTRACT II
Acknowledgement III
Table of Contents IV
List of Tables VI
List of Figures VII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research Scope 4
1.4 Outline of the Dissertation 5
Chapter 2 Related Work 7
2.1 Software Risks 10
2.1.1 Boehm’s work 15
2.1.2 Barki’s work 17
2.1.3 Sumner’s work 19
2.1.4 Schmidt’s work 21
2.1.5 Wallace’s work 24
2.1.6 Discussion of Software Risks 27
2.2 Software Risk Assessment 28
2.2.1 SEI Risk Assessment Method 30
2.2.2 DoD Risk Assessment Method 32
2.2.3 PMBOK Risk Assessment Method 34
2.2.4 Discussion of Risk Assessment Method 36
2.3 Clustering Technique 37
2.3.1 Hierarchical Cluster Analysis 38
2.3.2 K-Means Cluster Analysis 40
2.3.3 SPSS Two Step Cluster Analysis 41
2.3.4 Discussion of Clustering Technique 43
Chapter 3 Research Method 45
3.1 Research Process 45
3.2 The Quality of Instrument and Data Profile 51
3.2.1 Reliability and Validity of Instrument 51
3.2.2 Data Profile and Quality 53
Chapter 4 Exploring the Effects of Risk Components on Project Performance 55
4.1 Introduction 55
4.2 The Relationship of Risk Components in Risk Dimensions 56
4.3 The Relationship between Risk Dimensions and Impact 59
4.4 Patterns in Risk Across the Levels of Project Performance 63
Chapter 5 Exploring the Effects of Project Duration on Risk Exposure 69
5.1 Introduction
5.2 Data Clustering based on Project Duration 70
5.3 The Relationship between Project Duration and Risk Exposure 72
5.4 The Relationship between Project Duration and Risk Component 74
5.5 A Comparison of Significant Results 78
Chapter 6 Conclusion 81
6.1 Research Contribution 81
6.2 Future Work 81
6.3 Research Limit 82
References 83
Appendix A Journals Included in the Analysis 97
Appendix B The Instrument of Data Collection 99
Publication List 101
Curriculum Vitas 103
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