探索模組相依網絡與共同提交者網絡:以了解程式模組在生態系統中是否將被停止維護

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研究生：	李泓其 Li, Hong-Qi
論文名稱：	探索模組相依網絡與共同提交者網絡:以了解程式模組在生態系統中是否將被停止維護 Understanding Abandonment in Software Module Ecosystems: Exploring Dependency and Co-committer Network Information
指導教授：	雷松亞 Ray, Soumya
口試委員:	林福仁 LIN, FU-REN 王俊程 WANG, JYUN-CHENG
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 服務科學研究所 Institute of Service Science
論文出版年：	2018
畢業學年度：	106
語文別：	英文
論文頁數：	63
中文關鍵詞：	模組、停止維護、相依網絡、共同提交者網絡、GitHub 、RubyGems 、資料探勘
外文關鍵詞：	Module Abandonment, Co-committer Network
相關次數：	點閱：1 下載：0
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模組代表一組被打包好並能執行特定作業的程式碼，程式開發者可以使用模組中的功能與邏輯去實踐自己的軟體。當模組停止維護時，會導致那些使用它的應用程式與模組們也面臨潛在的危險，因此當開程式發者在選擇模組時，會避免選擇那些在未來將有可能停止維護的模組們。在的研究中，我們以Ruby程式語言為例，想幫助程式開發者們能更準確地分辨在未來可能被停止維護的模組們並且找出與停止維護相關的因子。在預測中，我們不只用了GitHub中模組開發端的資料與RubyGems中模組需求端的資料；我們更用了相依網絡與共同提交者網絡中的資料來一同預測模組的停止維護。透過了網絡中的資料，此研究能夠探索更佳的預測模型與了解網絡資料中與停止維護相關的因子。

Modules are packaged libraries of code. Developers can use import modules into their code to use its function and logic. Abandonment of module could cause several problems on those application and other modules depend on it. Developers try to avoid adopting a module that might be abandoned in near future. In this study, we want to help developers, who in the Ruby language ecosystem, identify module abandonment more precisely and discover important factors related abandonment for module maintainers could instantly reaction about it. To make the more precise prediction, we not only use both production and consumption side data of module from GitHub, RubyGems, and BestGems but extract information from dependency and co-committer network to predict abandonment of module. We discover an effective model for predicting module abandonment and find the network factors related to it.

Chapter 1 Introduction    1
Chapter 2 Module Ecosystems and Abandonment    6
1 Module Ecosystems    6
2 Abandonment    8
3 Previous Research    10
4 Established Gems Versus 1YO Gems    10
Chapter 3 Theory Building    12
1. Non-relational Information About Gems    12
2. Relational Network Information    13
1.1 Dependency Network    15
1.2 Co-committer Network    18
3. Established Gems V.S 1YO Gems    20
4. Predictive and Inferential Value of Network Information    21
Chapter 4 Data Collection and Preprocessing    22
1 Data Collection    22
1.1 Collecting data    22
1.2 Build Dependency Network    24
1.3 Build Co-committer Network    25
2 Preprocessing Data    27
2.1 Preprocessing Non-relational Data About Gems    27
2.2 Preprocessing Relational Network About Gems    30
Chapter 5 Analysis and Results    32
1 Overview of Data Analysis    32
1.1. Predictive Methods    32
1.2. Inferential Methods    36
2 Predicting Abandonment of Established Gems    37
2.1 Predictive Value of Network    38
2.2 Inferential Value of Network    39
3 Predicting Abandonment of 1YO Gems    41
3.1 Predictive Value of Network    41
3.2 Inferential Value of Network    42
Chapter 6 Discussion    43
1. Theoretical implication    44
1.1 Theoretical Implication of Predicting Abandonment of Established Gems    44
1.2 Theoretical Implication of Predicting Abandonment of 1YO Gems    46
2. Practical Implications    48
3. Limitations    49
4. Future Work    50
Reference    51
Appendix A    53
Appendix B    57
Appendix C    61
Appendix D    62


                                

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