臺灣博碩士論文加值系統

軟體群眾協同開發是一種透過網路平台，提供專案程式碼集中管理之服務。近幾年許多企業將以往只在內部開發的產品專案放到軟體群眾協同開發上，期待透過眾人的力量一起開發專案；但是，在軟體群眾協同平台上的專案，即使是著名的軟體公司專案，使用者關注數量也相差甚遠。因此在開發過程中，需要一個有效預測使用者關注數的方法，藉以了解如何規劃軟體群眾協同平台上的專案規
格設計，例如專案程式語言、專案負責人數或專案文件大小等等，能夠吸引更多使用者關注專案。
本研究以 Github 平台上人工智慧領域專案作為研究對象，將 Github 平台上的因子分成三個構面並設計模型，將資料集進行多層分群，各群透過多元迴歸分析建置預測模型。相較於過去靜態模型預測，本研究設計出一個動態模型，在專案開發過程中，動態加入使用者參與專案開發後之參數，進行預測模型調適。最後，以交叉驗證法進行驗證，得出以下結論：（1）加入使用者之預測模型比尚未考量使用者參與後的模型準確度高；（2）在專案開發過程中，群眾構面的影響程度會逐漸高於專案構面。因此，在關注人數預測模型中需要考量到軟體群眾參與之因素，而在專案開發後期投入的資源應從專案開發上，逐漸轉移至與使用者互動及專案議題討論上。

Software Crowdsourcing Collaboration Development is an online platform for providing centralized management of project code. In recent years, many enterprise projects are developed by software collaboration development are not internal development. They expect to develop the project through the power of crowd. However, there is extreme gap of the number of star between software projects even in the famous companies. Therefore, the project development needs a way to effectively predict the number of star and understand how to plan the project specification design, such as the program language, the number of project owners, or the size of the documentation, in order to attract more star in the project.
Compared with past static prediction models, our designed dynamic models can dynamically add the parameters of user participation and predict the adjustments of models. Finally, the prediction models is verified by cross validation. Research result：(1) The models which adds the parameters of user participation is more accurate than the models that does not. (2) The crowdsourcing construction will gradually be higher than the project construction on the impact of the number of star. Therefore, the prediction models of number of star needs to consider the user participation factors. In the late of project, the resources of project development should gradually transfer from the project development to the user interaction and project discussion

摘要.............................................................I
Abstract........................................................II
致謝...........................................................III
目錄............................................................IV
表目錄..........................................................VI
圖目錄.........................................................VII
第一章、緒論.....................................................1
1.1 研究背景 ....................................................1
1.2 研究動機 ....................................................2
1.3 研究目的 ....................................................4
1.4 研究流程 ....................................................5
第二章、文獻探討.................................................6
2.1 軟體群眾協同開發 ............................................6
2.1.1 相關平台介紹...............................................6
2.1.2 軟體群眾協同平台運作模式...................................7
2.2 軟體專案儲存庫探勘...........................................9
2.2.1 MSR 資料集介紹 ...........................................10
2.2.2 Github 相關名詞解釋 ......................................13
2.3 軟體群眾協同開發相關預測文獻 ...............................15
2.4 多元迴歸分析 ...............................................18
第三章、模式建構................................................21
3.1 研究模式 ...................................................21
3.2 專案關注數影響因子 .........................................23
第四章、預測模型建置與驗證......................................29
4.1 資料蒐集 ...................................................29
4.2 資料分析 ...................................................29
4.3 資料多層分群 ...............................................35
4.4 實驗結果 ...................................................40
4.5 討論與發現 .................................................49
第五章、結論與建議..............................................52
5.1 學術上的貢獻 ...............................................52
5.2 實務上的貢獻 ...............................................53
5.3 研究限制 ...................................................54
5.4 未來研究建議 ...............................................54
參考文獻........................................................55

Borges, H., Hora, A., & Valente, M. T.（2016, October）. Understanding the factors that impact the popularity of GitHub repositories. In Software Maintenance and Evolution (ICSME), 2016 IEEE International Conference on (pp. 334-344). IEEE.

Chen, F., Li, L., Jiang, J., & Zhang, L.（2014, May）. Predicting the number of forks for open source software project. In Proceedings of the 2014 3rd International Workshop on Evidential Assessment of Software Technologies (pp. 40-47). ACM.

Gousios, G., & Spinellis, D. （2012, June）. GHTorrent: Github's data from a firehose. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories （pp.12-21）. IEEE Press.

Izquierdo, J. C., Cosentino, V., & Cabot, J. （2015）. Attracting Contributions to your GitHub Project. The Journal of Object Technology

Jarczyk, O., Gruszka, B., Jaroszewicz, S., Bukowski, L., & Wierzbicki, A. （2014, November）. Github projects. quality analysis of open-source software. In International Conference on Social Informatics （pp. 80-94）. Springer International Publishing.

Jiang, J., Zhang, L., & Li, L. （2013, October）. Understanding project dissemination on a social coding site. In Reverse Engineering （WCRE）, 2013 20th Working Conference on （pp.132-141）. IEEE.

Kagdi, H., Collard, M. L., & Maletic, J. I. （2007）. A survey and taxonomy of approaches for mining software repositories in the context of software evolution. Journal of software maintenance and evolution: Research and practice, 19（2）, 77-131.

Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D. M., & Damian, D. (2014,May). The promises and perils of mining GitHub. In Proceedings of the 11th working conference on mining software repositories (pp. 92-101). ACM.


Nielek, R., Jarczyk, O., Pawlak, K., Bukowski, L., Bartusiak, R., & Wierzbicki, A. （2016,October）. Choose a Job You Love: Predicting Choices of Github developers. In Web Intelligence （WI）, 2016 IEEE/WIC/ACM International Conference on（pp. 200-207）.IEEE.

Pletea, D., Vasilescu, B., & Serebrenik, A. （2014, May）. Security and emotion: sentiment analysis of security discussions on Github. In Proceedings of the 11th working conference on mining software repositories （pp. 348-351）. ACM.

Saini, M., & Kaur, K. （2016）. Fuzzy analysis and prediction of commit activity in open source software projects. IET Software, 10（5）, 136-146

Tu, Q. （2000）. Evolution in open source software: A case study. In Software Maintenance, 2000. Proceedings. International Conference on （pp. 131-142）. IEEE.

Weber, S., & Luo, J. （2014, December）. What Makes an Open Source Code Popular on Git Hub?. In Data Mining Workshop （ICDMW）, 2014 IEEE International Conference on（pp. 851-855）. IEEE.

Yu, Y., Wang, H., Yin, G., & Ling, C. X. （2014, September）. Reviewer recommender of pull-requests in Github. In Software Maintenance and Evolution （ICSME）, 2014 IEEE International Conference on（pp.609-612）. IEEE.

Zhu, J., Zhou, M., & Mockus, A. （2014, September）. Patterns of folder use and project popularity: A case study of Github repositories. In Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement（p. 30）. ACM