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研究生: 莊博仁
Po-Jen Chuang
論文名稱: 支援永續性CMMI評鑑的系統之證據自動彙集設計
Assessment Evidence Auto-Aggregating Design for the Continual CMMI Software Process Improvement Assessments Supporting System
指導教授: 王孔政
Kung-Jeng Wang
口試委員: 林義貴
Yi-Kuei Lin
郭伯勳
Po-Hsun Kuo
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2009
畢業學年度: 97
語文別: 英文
論文頁數: 52
中文關鍵詞: CMMISoftware Process ImprovementAssessmentAuto-aggregating
外文關鍵詞: CMMI, Software Process Improvement, Assessment, Auto-aggregating
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Although Capability Maturity Model Integration (CMMI) is well known adopted by many software development organizations in present, the effort of the continual CMMI assessment is extremely exhausted. Even though previous research effectively raises the efficiency for the continual CMMI software process improvement (SPI) assessment, the assessment artifacts of document types or tool-based summarized data types as the assessment evidence still needs the manual aggregating by collecting and checking. It requires an automatic capability for aggregating assessment artifact, instead of the manual aggregation. Accordingly, an object-oriented approach is utilized in the study to analyze and design the assessment evidence auto-aggregating system that can automatically collect and check the assessment evidences of document type or tool-based summarized data type as the work product resulted from the implemented process of the assessment organization units or projects is developed. Furthermore, a related system prototype is constructed and demonstrated to illustrate the practically operational scenario and procedure. It shows the usability, effectiveness and the helpfulness of the assessment evidence auto-aggregating. Through the supporting of the proposed system, a significant amount of the effort of the assessment preparation for assessment evidence aggregating can be largely reduced, therefore facilitate the execution of the assessment in considerable resource and time saving.


Although Capability Maturity Model Integration (CMMI) is well known adopted by many software development organizations in present, the effort of the continual CMMI assessment is extremely exhausted. Even though previous research effectively raises the efficiency for the continual CMMI software process improvement (SPI) assessment, the assessment artifacts of document types or tool-based summarized data types as the assessment evidence still needs the manual aggregating by collecting and checking. It requires an automatic capability for aggregating assessment artifact, instead of the manual aggregation. Accordingly, an object-oriented approach is utilized in the study to analyze and design the assessment evidence auto-aggregating system that can automatically collect and check the assessment evidences of document type or tool-based summarized data type as the work product resulted from the implemented process of the assessment organization units or projects is developed. Furthermore, a related system prototype is constructed and demonstrated to illustrate the practically operational scenario and procedure. It shows the usability, effectiveness and the helpfulness of the assessment evidence auto-aggregating. Through the supporting of the proposed system, a significant amount of the effort of the assessment preparation for assessment evidence aggregating can be largely reduced, therefore facilitate the execution of the assessment in considerable resource and time saving.

Abstract I List of Figures IV CHAPTER 1. INTRODUCTION 1 1.1 Background 1 1.2 Motivation 2 1.3 Structure of Thesis 4 CHAPTER 2. LITERATURE SURVEY 6 2.1 Capability Maturity Model Integration 6 2.2 Software Process Assessment 10 2.3 Auto-aggregating 13 2.4 Summary 14 CHAPTER 3. OBJECT-ORIENTED ANALYSIS AND DESIGN OF THE CMMI ASSESSMENT EVIDENCE AUTO-AGGREGATING SYSTEM 15 3.1 System overview 15 3.2 System context mode 18 3.3 System requirements analysis 21 3.4 Aggregate artifact design 25 CHAPTER 4. SYSTEM PROTOTYPE 32 4.1 Define CMMI Practice Evidence 37 4.2 Auto-aggregate Artifact 39 4.3 Manage Auto-aggregating Results 43 CHAPTER 5. CONCLUSION 48 REFERENCES 50

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