通常也會使用另一種語言，例如:網頁前端技術中，會 javascript 的開發人員，通常 也會使用 jquery 語言等。
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題，透過分析出潛在使用者，可了解到目前專案的發展情況，例如:本研究 4.4 小節 專案演進階段，再細分出偶爾使用的使用者，或是隔一段時間突然使用的使用者，
列為潛在客戶變數，分析當吸引力高、潛在客戶少，或是黏著度低、潛在客戶多等 情況。本研究使用專案的吸引力與黏著度區分出專案階段的四個象限(活躍期、流動 期、穩定期、衰退期)，未來可加入潛在客戶變數，列入第三種維度，便可重新定義 專案階段，利用不同的專案演進階段進行更精細之分析。
第三、建立 GitHub 平台上的推薦技術，根據適合使用者的專長技術，推薦適合 的開發專案。近幾年來推薦系統的發展多元，涵蓋多種領域，各式各樣的推薦主題 孕育而生。所謂的推薦系統是依據使用者的喜好、興趣，或是使用行為，來推薦使 用者可能會感興趣的商品或資訊。未來可利用此概念，利用在 GitHub 平台上，以本 研究為例，在第四章節中，藉由分析專案演進可得知專案的興盛與衰退的過程，便 可利用此資訊進行專案的推薦，同時過濾與分類使用者的專長技術，甚至是所屬組 織，將目前處於活躍期的專案推薦給合適的使用者，甚至利用專案的演進推估專案 未來趨勢，便可提高推薦精準度，讓使用者在 GitHub 上更容易的找到所需的資訊。
第四、GitHub 資料集中與 TIOBE 程式語言排名的差異分析。隨著近幾年數據 分析的興起，在 TIOBE 中已可看出 MATLAB、R 等利於統計分析的程式語言排名 漸漸上升，然而本研究在 2.5.2 小節中比較了 GitHub 資料集中與 TIOBE 的排名差異，
在比較中發現 GitHub 資料集中未發現此類統計相關程式語言有成長的現象，資料集 中目前仍以主流程式語言為主，如 java、ruby 等，未來研究可針對此議題進行更進 一步的分析與研究，並且強調針對 GitHub 資料集上的分析，更能反應出主流的真實 情況，以增進研究的可信度與研究價值。
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