資訊系統對於進行數位轉型並提供線上服務的企業來說非常關鍵。良好的專案管理能夠讓資訊系統有效的適應不斷變化的需求。然而，隨著這些系統變得越來越複雜，適應性會隨之下降，並且可能會降低對企業效益的回報。因此，如何在早期就評估程式碼的適應性尤其重要。我們的目標是開發一個衡量指標用以量化程式碼的適應性，而該衡量指標會結合五種著名的軟體品質指標。為了證實我們的結構模型範疇並驗證程式碼適應性確實對專案後期的程式碼活動度有所影響，我們對此進行了建模。我們透過蒐集開源程式碼的軟體專案資料來估計我們的模型，該資料集來自於 Ruby 程式語言的生態系統。我們發現程式碼適應性對程式碼活動度有長期的影響，這意味著在早期階段就需要考慮程式碼的適應性。此外，程式碼適應性對程式碼活動度的影響在多年後仍然會持續著。這些發現說明了我們建立了一個有效且有用的程式碼適應性評估工具，可以廣泛的應用在許多領域的研究和實踐上。

Software systems are vital for businesses undergoing digital transformation and offering online services. Effective project management is crucial for the success of software systems to adapt to changing requirements. However, as these systems become more complex, adaptability declines and the returns to firm performance can decrease. Therefore, how to evaluate code adaptability at an early stage is important. We set about to develop a measure to quantify code adaptability, using five established software quality metrics. To validate the nomology of our construct, and verify the oft-held belief that code adaptability matters, we modeled it relates to later code activity of a project. We estimated our model on a dataset of open source software packages for the Ruby programming ecosystem. We found that code adaptability has a long-term impact on code activity, implying the importance of considering code adaptability in the early stages. Moreover, the effect of code adaptability on code activity does not diminish significantly over the years. These findings suggest that we have constructed a valid and useful measurement of code adaptability that can be used in various areas of research and practice across many ecosystems.

摘要-----------------------------------------------------------------------------3
Abstract------------------------------------------------------------------------4
Table of Contents---------------------------------------------------------------6
Chapter 1. Introduction---------------------------------------------------------7
Chapter 2. Quantifying Code-----------------------------------------------------10
2.1 Code Adaptability-----------------------------------------------------------10
2.2 Code Activity---------------------------------------------------------------18
Chapter 3. Proposed Measurement and Structural Models---------------------------20
3.1 Ruby Gems-------------------------------------------------------------------20
3.2 Measuring Code Adaptability Using Static Analysis---------------------------21
3.2.1 Rubocop-------------------------------------------------------------------22
3.2.2 Flog----------------------------------------------------------------------23
3.2.3 Gunning Fog Index---------------------------------------------------------23
3.2.4 Reek----------------------------------------------------------------------24
3.3 Code Adaptability and Activity----------------------------------------------24
Chapter 4. Empirical Study------------------------------------------------------26
4.1 Data Collection-------------------------------------------------------------26
4.2 Data Post-processing--------------------------------------------------------29
4.3 Exploration of Code Quality Trend-------------------------------------------30
4.4 Principal Component Analysis (PCA) for exploratory measurement modeling-----34
4.5 Partial Least Squares (PLS) for Emerging Variables--------------------------37
Chapter 5. Discussion-----------------------------------------------------------41
Chapter 6. Future Work and Conclusions------------------------------------------43
References----------------------------------------------------------------------45

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