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研究生： 曹朱榜
Chu-pong Tsaou
論文名稱： 使用模糊理論於目標驅動使用案例之功能點分析研究
The Study on Function Point Analysis of Goal-Driven Use Cases using Fuzzy Theory
指導教授： 李允中
Jonathan Lee
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 97
語文別： 中文
論文頁數： 70
中文關鍵詞： 目標驅動 、軟體估算 、功能點 、模糊理論 、使用案例點
外文關鍵詞： GART, FUZZY, Use Case, Function Point
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    • 傳統之使用案例功能點分析針對使用案例的複雜度只給予三個固定的權值，因而忽略了個別使用案例的複雜程度。本研究提出一個使用案例的度量程序，使用模糊理論於目標驅動使用案例以進行傳統使用案例功能點分析方法之擴充。根據使用案例之間的關聯程度，我們使用模糊歸屬函數與模糊規則進行模糊推導，計算出個別使用案例之未調整目標驅動使用案例功能點（Unadjusted GDUC Points）。之後，進一步考慮系統與環境的複雜因子，計算整個系統調整後之目標驅動使用案例功能點。如此可更精確的表示出個別使用案例的複雜度並進一步了解整體系統之複雜度與所需的開發時程與人力。

    Traditional use case point analysis method provides only three fixed complexity grades for use case which can not show the exact complexity of each use case in a system. This study, therefore, provides a use case measurement procedure for goal-driven use cases based on fuzzy theory. We propose a membership function and fuzzy rules to calculate the unadjusted function points of goal-driven use cases (Unadjusted GDUC points) based on the relations between each use cases. Furthermore, the technical and environmental factors are considered to calculate the adjusted use case points of the system. The adjusted GDUC points can be used to estimate the implementation time and effort of the system under development.

    摘要 III Abstract IV 誌謝 V 第一章緒論 1 1.1背景 1 1.2動機 3 1.3論文架構 3 第二章 背景知識介紹 5 2.1 程式行度量法（Line Of Code） 6 2.2 功能點分析度量法(Function Point Analysis) 6 2.3 MKII 10 2.4 使用案例點分析(Use Case Function Point Analysis) 15 2.4.1統一模塑語言(Unified Modeling Language, UML) 15 2.4.2 使用案例點(Use Case Point, UCP) 17 2.5目標驅動之需求追溯方法[11] 23 2.6 模糊理論(Fuzzy Theory) 28 2.4.1模糊集合(Fuzzy set ) 29 2.4.2 歸屬函數(Function membership) 30 2.4.3模糊規則(Fuzzy Rule) 31 2.4.4 模糊推論 31 2.4.5 去模糊化 34 2.4.6 模糊控制 35 第三章研究方法 36 3.1 指定參與者和使用案例複雜度 (Assign Actor Weight and Unadjusted Use Case Point Weight) 37 3.2 計算U2G滿足值 (Calculate U2G Satisfaction Value) 37 3.3 進行模糊化計算(Calculate Fuzzy GDUC Points with Fuzzy Rules) 39 3.4 計算技術因子和環境因子 41 3.5 計算已調整目標驅動使用案例功能點 (Calculate Adjusted GDUC Points) 42 第四章案例分析 43 4.1傳統使用案例點計算 44 4.2 計算GDUC步驟 45 第五章 相關研究 51 5.1 類神經網路與模糊系統對功能點分析計算法（A Neuro-Fuzzy Model for Function Point Calibration）[12] 51 5.1.1 Fuzzy Logic部分 52 5.1.2 Neural network部分 54 5.1.3 確認結果 54 5.1.4 Mean Magnitude Relative Error (MMRE) 54 5.1.5 結論 55 5.2 使用案例之軟體成果估算(Software Effort Estimation Based on Use Cases )[13] 55 5.2.1 模糊化語意群(Fuzzyfication of linguistic terms) 55 5.2.2 結論 57 第六章結論 58 參考文獻 59

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