在開源軟體工程的領域中，模組代表一組被打包好並且能夠執行特定作業的程式碼，也因為是開源的關係，其他任何的開發者都能夠將模組利用於自己的程式中。因為大部分的模組都是依賴於其他許多的模組之上，因此模組是否被維護者停止維運變成開發者在使用它之前的一項考量因素。此研究包含兩個目標：第一，我們希望能夠提供有意義的資訊來讓開發者作為評估模組是否被維護者所拋棄的參考依據；第二，我們希望幫助自行管理程序模組的社群檢視需要花龐大資源來收集的資料的價值。在此研究中，我們以 Ruby 程式語言作為研究及分析的對象。此外，我們希望能夠預測出 Ruby 模組在未來一年是否會被其維護者停止維護，我們認為 GitHub 及RubyGems 能提供我們 Ruby 模組全方面的資訊，因此我們便將此兩平台作為資料來源，並且利用資料探勘的方法建立分類模型。最後，我們發現能夠有效預測模組是否停止維運的分類模型，以及可能和其有關的因素。另外，我們也瞭解到花龐大資源來儲存昂貴的使用下載資訊的並不是必要的。更進一步地，我們替 Ruby 社群建立了線上儀表板系統，在系統當中，利用視覺化的方式呈現 Ruby 模組在 GitHub 及 RubyGems 的資訊，以及從我們建立的分類模型中產出對停止維運的評估指標。

Open source modules, which are software libraries, are often abandoned by their maintainers. This abaondonment becomes a serious concern for developers and for software projects at large. In this study, we want to first help developers recognize module abandonment in a data-driven manner. Second, we wish to help the sites which host modules to examine the value of preserving expensive usage data. We examine the modules of the Ruby programming language in this study. We try to predict if gems will be abandoned in the near future by analyzing data from GitHub and RubyGems using data mining techniques. We discover effective models to predict abandonment and factors associated with it. Moreover, we begin to percive that it may not be necessary to collect expensive usage data from independent module hosting communities to predict module abandonment. Using these results, we build an online dashboard to help Ruby developers recognize potential abandonment of modules. This study helps us to combined theoretical methods with practical use.

Chapter 1 Introduction - 1 -
Chapter 2 Modules and Gems - 5 -
2.1 How gems are created by developers? - 5 -
2.2 How are gems used by other developers? - 7 -
Chapter 3 Module Ecosystems - 9 -
3.1 Gem Dependencies - 9 -
3.2 Gem Abandonment - 11 -
3.3. Different Module Ecosystems - 12 -
3.3.1 GitHub Hosted Module Ecosystem - 13 -
3.3.2 Community Hosted Module Repository - 14 -
3.4 Recognizing Abandonment as a Practitioner - 16 -
Chapter 4 Research on Abandonment - 19 -
4.1 Previous Abandonment Research - 19 -
4.2 Research Gaps - 22 -
Chapter 5 Data Collection and Preprocessing - 25 -
5.1 How to Collect the Data - 25 -
5.2 Data Schema - 27 -
5.3 Data Preprocessing - 29 -
5.3.1 Data Conversion and Labeling - 29 -
5.3.2 Difficulty of Removing Contaminated Data - 34 -
5.3.3 Multiple Datasets - 35 -
5.4 Challenges - 35 -
Chapter 6 Data Exploration and Analysis - 38 -
6.1 Data Exploration - 38 -
6.2 Data Analysis - 39 -
6.2.1 GitHub-Only Dataset - 43 -
6.2.2 RubyGems-Only Dataset - 44 -
6.2.3 Combined GitHub + RubyGems Dataset - 46 -
Chapter 7 Findings and Discussion - 49 -
7.1 Discussion of Research Questions - 49 -
7.1.1 How Can We Effectively Predict Abandonment of Modules? - 49 -
7.1.2 Which Factors Might Correlate with Abandonment of Modules? - 52 -
7.1.3 Is GitHub Data Enough for Predicting Abandonment of Modules? Or Should Independent Module Repositories Collect Usage Data? - 55 -
7.2 Discussion of Objectives - 56 -
7.2.1 Helping Developers to Recognize Gem Abandonment - 57 -
7.2.2 Considering the Storage of Expensive Data - 59 -
Chapter 8 Limitations and Future Work - 60 -
Chapter 9 Conclusion - 61 -
References - 62 -
Appendix A - 66 -
Appendix B - 68 -

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