近十年來，軟體對我們的生活而言是絕對必需品。軟體開發生命週期對軟體工程師而言是用來生產軟體的流程，在軟體開發生命週期的階段中，軟體維護佔了多數成本，此外在長時間的維護活動之後，軟體架構總是會變形、軟體大小與複雜度會隨之提升、品質也會變差。其中一個解決這些問題的方案為軟體模組化，這是一個減少時間成本、提升品質的有效方法。而叢集(Clustering)是種直覺的方式來實作模組化並將程式碼分類為更小的區塊，然而傳統的叢集演算法有些缺點，例如未定義的模組數量、模組化結果可理解性很低等等，因此許多這個領域的研究嘗試找出不同的特徵、不同的相似度計算方式或是一些特殊的演算法來提升效能。
　　在這份研究中，我們提出多特徵叢集演算法(MPC)來作軟體架構回復。在這個演算法中共有五個主要步驟，分別為前置作業、標記檔案、連鎖關聯的收集、階層凝聚行的演算法、修正叢集結果。在實驗中，我們使用了擁有不同複雜度與用途的三種開源程式系統與一個閉源程式系統，比較MPC 演算法與其他傳統軟體叢集分析工具的效能，例如單一連結演算法、完整連接演算法、未加權連接演算法、加權連接演算法、聯合演算法與理解驅動叢集演算法，評估結果顯示MPC 演算法的品質比專家切割的模組化品質好約莫1.6倍，此外，比起其他演算法的結果與人類思維的相似度，MPC 演算法平均而言分別提升13%。由此證明MPC 演算法對人類理解而言是種更有效的叢集工具，此外他也比這些演算法中能產出更好的模組品質。

In recent decades, software has become absolutely necessary for our life. Software development lifecycle (SDLC) is a process for software engineers to produce software. Within the steps of SDLC, software maintenance occupies most of the total cost of software. Additionally, the systems’ structure always exacerbates after extended maintenance activities, in that the size and complexity of systems increases and that quality usually degrades. One solution is software modularization, which is a beneficial way to reduce the time and cost and to enhance the quality. Clustering is an intuitive way to do modularization and classify codes into small pieces. However, traditional clustering algorithms have some drawbacks such as the undefined number of modules, low understandability for the modularization results, etc., so a lot of research in this field tries to use different features, different similarity calculation, or some special algorithms to enhance the performance.
In this study, we propose a multi-pattern clustering (MPC) algorithm to do software architecture recovery. There are five main steps in the MPC algorithm: preprocessing, file labeling, collection of chain dependency, hierarchical agglomerative algorithm, and modification of the clustering result. In our experiments, we compare the performance of the MPC algorithm to some traditional software clustering techniques, such as the single linkage algorithm, the complete linkage algorithm, the unweighted linkage algorithm, the weighted linkage algorithm, the combined algorithm, and the algorithm for comprehensive-driven clustering with three open-source software and one closed-source software programs, and each of them have different sizes and usages. The assessment results show that the modularization quality of the proposed MPC algorithm is nearly 1.6 times better than that of expert decomposition. Additionally, compared to other algorithms, the MPC algorithm, on average, has a 13% enhancement in producing results similar to human thinking. Consequently, it has been proven that the proposed MPC algorithm is the more suitable clustering technique for human comprehension, and it produces the better module quality compared to these algorithms.

Abstract i
Abstract in Chinese iii
Acknowledgement iv
Contents v
List of Tables vi
List of Figures vii
List of Symbols viii
Acronyms and Abbreviations viii
Notation ix
Chapter 1 Introduction 1
Chapter 2 Background and Literature Review 6
2.1 Basic Concepts of Hierarchical Agglomerative Clustering 6
2.2 Review of Software Modularization Techniques 10
Chapter 3 Modularization with Multi-Pattern Clustering 16
3.1 Step 1: Preprocessing 17
3.2 Step 2: File Labeling 22
3.3 Step 3: Collection of Chain Dependency 28
3.4 Step 4: Hierarchical Agglomerative Algorithm 28
3.5 Step 5: Modification of Clustering Result 29
Chapter 4 Experimental Results and Analysis 32
4.1 Test Systems 32
4.2 Comparison Criteria 32
4.2.1 Weighted Modularization Quality 33
4.2.2 Metrics: Accuracy, Precision, Recall and F1-Measure 34
4.2.3 MoJoFM 36
4.3 Expert Decomposition Data 37
4.4 Experimental Results and Discussions 37
4.5 Observations and Suggestions for Software Development and Maintenance 47
4.6 Visualization Tool for Software Modularization - ModulePlot 50
4.7 Threats to Validity 53
Chapter 5 Conclusions and Future Work 57
Reference 59
Appendix 64

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