在軟體反向工程中以本體論為基礎建立一套設計品質評核之方法-以複雜度為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：40

、訪客IP：3.137.181.194

姓名

張德芳(De-Fang Chang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

在軟體反向工程中以本體論為基礎建立一套設計品質評核之方法-以複雜度為例
(Design and Implementation of Ontology-based Evaluation System for Design Quality in Software Reverse Engineering: Focusing on Complexity)

相關論文

★ 專案管理的溝通關鍵路徑探討─以某企業軟體專案為例	★ 運用並探討會議流如何促進敏捷發展過程中團隊溝通與文件化：以T銀行系統開發為例
★ 專案化資訊服務中人力連續派遣決策模式之研究─以高鐵行控資訊設備維護為例	★ 以組織正義觀點介入案件指派決策之研究
★ 應用協調理論建立系統軟體測試中問題改善之協作流程	★ 應用案例式推理於問題管理系統之研究 -以筆記型電腦產品為例
★ 運用限制理論於多專案開發模式的人力資源配置之探討	★ 應用會議流方法於軟體專案開發之個案研究：以翰昇科技公司為例
★ 多重專案、多期再規劃的軟體開發接案決策模式：以南亞科技資訊部門為例	★ 會議導向敏捷軟體開發及系統設計：以大學畢業專題為例
★ 一種基於物件、屬性導向之變更影響分析方法於差異化產品設計	★ 會議流方法對大學畢業專題的團隊合作品質影響之實驗研究
★ 實施敏捷式發展法於大學部畢業專題之行動研究 – 以中央大學資管系為例	★ 建立一個用來評核自然語言需求品質的線上資訊系統
★ 結合本體論與模糊分析網路程序法於軟體測試之風險與風險關聯辨識	★ 在軟體反向工程中針對UML結構模型圖之線上品質評核系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2026-7-1以後開放)

摘要(中)

為因應資訊快速發展，軟體開發商需在短時間內完成軟體開發以提升其市場競爭力，這也導致其無法在完整的系統分析下投入開發。因此，軟體開發者可使用軟體反向工程 (SRE) 工具令開發完的軟體快速產出系統設計文件。然而，若倉促間實作的程式碼品質不佳，會進而影響SRE產出之UML圖的品質。為解決此問題，本研究針對SRE產出之設計文件與程式碼品質開發一個系統。本研究針對軟體的複雜度品質進行探討，並應用程式碼氣味與反面模式以發展複雜度的Rule-based偵測模式，並結合程式碼層級與設計層級的指標綜合分析系統之複雜度品質。最後，根據系統品質評核的結果提出重構建議。此外本研究將運用本體論以建立品質評核的知識庫，並且實作一套 Web-based的軟體設計品質評核系統，再透過一個專案來展示系統，並用五個測試案例驗證該系統的功能與效益。

摘要(英)

In response to the rapid development of information, software developers need to complete software within a shorter time to enhance their competitiveness. As a result, the software development team cannot go through a complete system analysis process before implementing the software. Therefore, software developers can use reverse engineering (SRE) tools to quickly produce system design files for the developed software. However, if the hastily implemented code brings to poor/bad quality, it will consequently affect the quality of UML diagrams produced by SRE. To solve this problem, this study develops a system for the quality of design documents produced by SRE and the code of the project. This study focuses on the complexity quality of the software, and applies the code smells and anti-patterns to develop the rule-based detection for complexity, and also combines the code-level and design-level metrics to comprehensively analyze the system’s complexity quality. Finally, refactoring suggestions are made based on the results of the quality assessment for the system. In addition, this study uses ontology to build a knowledge base for quality assessment and implements a Web-based software design quality evaluation system. Furthermore, this study demonstrates the system through a project and uses five test cases to verify the system’s accumulative performance and benefits.

關鍵字(中)

★ 軟體反向工程
★ 本體論模型
★ 複雜度
★ UML結構圖
★ 重構

關鍵字(英)

★ Software Reverse Engineering
★ Ontology Model
★ Complexity
★ Structural UML Diagram
★ Refactoring

論文目次

摘要......vii
Abstract......viii
目錄......ix
圖目錄......xi
表目錄......xii
一、緒論......1
1-1 研究背景......1
1-2 研究問題與動機......1
1-3 研究目的......3
1-4 研究範圍與假設......4
1-5 研究架構......5
二、文獻探討......6
2-1 軟體反向工程......6
2-1-1 UML 反向工程研究與工具......6
2-1-2 UML反向工程之品質......7
2-2 軟體複雜度與其指標......8
2-3 複雜度缺陷&重構......9
2-3-1程式碼與設計氣味......9
2-3-2 重構......11
2-4 本體論......11
三、研究方法......14
3-1 系統架構......14
3-2 資料擷取......15
3-3 本體建置......17
3-4 複雜度識別規則......20
3-5 重構方法......31
四、系統實作與展示......33
4-1 系統與開發環境......33
4-2 系統展示......35
4-2-1 系統預備與資料擷取......36
4-2-2 品質檢測與結果呈現......42
五、系統成果與討論......45
5-1方法設計評估......45
5-2 系統與結果評估......46
5-3 系統可靠度評估......47
5-3-1 規則- R1 Data Class......49
5-3-2 規則- R2 Large Class與R3 Blob......49
5-3-3 規則- R6 Long Method與R12 Spaghetti Code......50
5-3-4 規則- R7 Refused Bequest......51
5-3-5 規則- R8 Speculative Generality......52
5-3-6 規則- R9 Lava Flow......52
5-3-7 規則- R10 Functional Decomposition......52
5-4 系統數值分析驗證......53
5-5 系統累積性能評估......54
5-6 評估的效度分析......57
六、結論......58
6-1 研究貢獻......58
6-2 研究限制與未來發展......59
參考文獻......60

參考文獻

[1] 陳仲儼與徐子涵 (2019)。建立一個在軟體反向工程中針對 UML 結構圖形之自動化本體品質檢測系統：以耦合度為例，中華民國資訊管理學報，第二十六巻，第四期，頁 379-412。
[2] 陳仲儼與蔡鴻儒 (2014)。應用本體論來線上展開資訊系統潛藏與知識密集之品質需求，資訊管理學報，第二十一卷，第一期，頁45-82。
[3] 楊芝瑩 (2017)。在軟體反向工程中針對UML結構模型圖之線上品質評核系統（未出版之碩士論文）。國立中央大學，桃園市。
[4] 葉圻煒 (2019)。在軟體反向工程中應用本體論架構建立一套設計品質評核之方法-以安全性為例（未出版之碩士論文）。國立中央大學，桃園市。
[5] 鍾幸軒 (2019)。在軟體反向工程中以本體論為基礎建立一套設計品質評核之方法-以內聚力為例（未出版之碩士論文）。國立中央大學，桃園市。
[6] Abbot, D. (1993). A design complexity metric for object-oriented development. Unpublished Master’s Thesis, Department of Computer Science, Clemson University, USA.
[7] Al Dallal, J., Briand, L.C. (2010). An object-oriented high-level design-based class cohesion metric. Information and Software Technology, 52(12), 1346-1361
[8] Al Dallal, J. (2012). Fault prediction and the discriminative powers of connectivity-based object-oriented class cohesion metrics, Information and Software Technology, 54(12), 396-416.
[9] Aras, M. T., & Selçuk, Y. E. (2016). Metric and rule based automated detection of antipatterns in object-oriented software systems. In: The 7th International Conference on Computer Science and Information Technology (CSIT), 1-6. IEEE, Amman, Jordan.
[10] Assunçãoa, W.K.G., Vergilio, S.R. and, Lopez-Herrejon, R.E. (2019). Automatic extraction of product line architecture and feature models from UML class diagram variants. Information and Software Technology, 117, 106198.
[11] Bagheri, E., & Gasevic, D. (2011). Assessing the maintainability of software product line feature models using structural metrics. Software Quality Journal, 19(3), 579-612.
[12] Baorto, D., Li, L., & Cimino, J. J. (2009). Practical experience with the maintenance and auditing of a large medical ontology. Journal of Biomedical Informatics, 42(3), 494-503.
[13] Basili, V.R., & Caldiera, G. (1995). Improve software quality by reusing knowledge and experience. MIT Sloan Management Review, 37(1), 55-55.
[14] Bavota, G., De Lucia, A., Di Penta, M., Oliveto, R., & Palomba, F. (2015). An experimental investigation on the innate relationship between quality and refactoring. Journal of Systems and Software, 107, 1-14.
[15] Bigonha, M., Ferreira, K., Souza, P., Sousa, B.L., Januário, M., & Lima, D. (2019). The usefulness of software metric thresholds for detection of bad smells and fault prediction. Information and Software Technology, 115, 79-92.
[16] Borst, W. N., & Borst, W. N. (1997). Construction of engineering ontologies for knowledge sharing and reuse. Department of Computer Science, Subdepartment of Information Systems, University of Twente, the Netherlands.
[17] Briand, L. C., Labiche, Y., & Leduc, J. (2006). Toward the reverse engineering of UML sequence diagrams for distributed Java software. IEEE Transactions on Software Engineering, 32(9), 642-663.
[18] Broniatowski, D. A., & Tucker, C. (2017). Assessing causal claims about complex engineered systems with quantitative data: Internal, external, and construct validity. Systems Engineering, 20(6), 483-496.
[19] Brown, W.H., Malveau, R.C., McCormick, H.W., & Mowbray, T.J. (1998). AntiPatterns: Refactoring software, architectures, and projects in crisis. Canada: Robert Ipsen.
[20] Canfora, G., Penta, M.D., & Cerulo, L. (2011). Achievements and challenges in software reverse engineering. Communications of the ACM, 54(4), 142-151. New York, USA.
[21] Chen, R. C., Huang, Y. H., Bau, C. T., & Chen, S. M. (2012). A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection. Expert Systems with Applications, 39(4), 3995-4006.
[22] Chi, Y. L. (2009). Ontology-based curriculum content sequencing system with semantic rules. Expert Systems with Applications, 36(4), 7838-7847.
[23] Chidamber, S., & Kemerer, C. (1994). A metrics suite for object oriented design. IEEE Transactions on Software Engineering, 20, 476-493.
[24] Chikofsky, E. J., & Cross, J. H. (1990). Reverse engineering and design recovery: A taxonomy. IEEE software, 7(1), 13-17.
[25] Chowdhurya, I. and Zulkernineb, M. (2011). Using complexity, coupling, & cohesion metrics as early indicators of vulnerabilities. Journal of Systems Architecture, 57(3), 294-313.
[26] CODEWITHC (2021). Programming: Projects & source codes. Retrieved from: https://www.codewithc.com/ (2021.06.01).
[27] Danphitsanuphan, P., & Suwantada, T. (2012). Code smell detecting tool and code smell-structure bug relationship. In: 2012 Spring Congress on Engineering and Technology, 1-5. IEEE, Xi′an, China.
[28] de Almeida Biolchini, J. C., Mian, G., Natali, A. C. C., Conte, T. U., & Travassos, G. H. (2007). Scientific research ontology to support systematic review in software engineering. Advanced Engineering Informatics, 21(2), 133-151.
[29] Di Lucca, G.A., Fasolino, A.R., & Tramontana, (2004). Reverse engineering web applications: The WARE approach. Journal of Software Maintenance and Evolution: Research and Practice, 16(1‐2), 71-101.
[30] Dingsøyr, T., Moe, B.N., Seim, A.E. (2018). Coordinating knowledge work in multiteam programs: Findings from a large-scale agile development program. Project Management Journal, 49(6), 64-77.
[31] Di Nucci, D., Palomba, F., De Rosa, G., Bavota, G., Oliveto, R., & De Lucia, A. (2017). A developer centered bug prediction model. IEEE Transactions on Software Engineering, 44(1), 5-24.
[32] Eclipse. (2019). Eclipse: The platform for innovation and collaboration. Retrieved from: https://www.eclipse.org/ (2021.06.01).
[33] Fensel, D. (2001). Ontologies. In: Ontologies, 11-18. Springer, Berlin, Germany.
[34] Fernández-Sáez, A.M., Genero, M., Chaudron, M.R., Caivano, D., & Ramos, I. (2015). Are forward designed or reverse-engineered UML diagrams more helpful for code maintenance? : A family of experiments. Information and Software Technology, 57, 644-663.
[35] Ferreira, K. A., Bigonha, M. A., Bigonha, R. S., Mendes, L. F., & Almeida, H. C. (2012). Identifying thresholds for object-oriented software metrics. Journal of Systems and Software, 85(2), 244-257.
[36] Filó, T. G., Bigonha, M., & Ferreira, K. (2015). A catalogue of thresholds for object-oriented software metrics. In: Proceedings of the first International Conference on Advances and Trends in Software Engineering (SOFTENG), 48-55, Barcelona, Spain.
[37] Fourati, R., Bouassida, N., & Abdallah, H.B. (2011). A Metric-Based Approach for Anti-pattern Detection in UML Designs. In: International Conference on Computer and Information Science, 17-33, Sanya, China.
[38] Fontana, F., Mäntylä, M., Zanoni, M., & Marino, A. (2015). Comparing and experimenting machine learning techniques for Code smell detection. Empirical Software Engineering, 21, 1143-1191.
[39] Fowler, M., & Beck, K. (1999). Refactoring - improving the design of existing code. Book, Addison Wesley object technology series.
[40] Franke, D., Elsemann, C., Kowalewski, S., & Weise, C. (2011). Reverse engineering of mobile application lifecycles. In: The 18th Working Conference on Reverse Engineering, 283-292. IEEE, Lero, Ireland.
[41] Gabriele, B., Andrea, D.L, Andrian, M., Rocco, O. (2013). Using structural and semantic measures to improve software modularization, Empirical Software Engineering, 18, 901-932.
[42] Gahalaut, A. K., & Khandnor, (2010). Reverse engineering: an essence for software re-engineering and program analysis. International Journal of Engineering Science and Technology, 2(6), 2296-2303.
[43] Genero, M., Manso, E., Visaggio, A., Canfora, G., & Piattini, M. (2007). Building measure based prediction models for UML class diagram maintainability. Empirical Software Engineering, 12(5), 517-549.
[44] Gonzalez, R. (1995). A unified metric of software complexity: Measuring productivity, quality, and value. Journal of Systems and Software, 29, 17-37.
[45] Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220.
[46] Guarino, N. (1998). Formal ontology in information systems. In: Proceedings of the first International Conference, June 6-8, Trento, Italy
[47] Ibrahim, R., Ahmed, M., Nayak, R., & Jamel, S. (2020). Reducing redundancy of test cases generation using code smell detection and refactoring. Journal of King Saud University-Computer and Information Sciences, 32, 367-374.
[48] IntelliJ IDEA (2021) IntelliJ IDEA: The capable & ergonomic Java IDE by JetBrains. Retrieved from: https://www.jetbrains.com/idea/ (2021.06.01).
[49] Jain, A., Nandakumar, K., & Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38(12), 2270-2285.
[50] Jakhar, A.K., Rajnish, K. (2016). A cognitive measurement of complexity and comprehension for object-oriented code. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 10(3), 643-650.
[51] Kang, D., Xu, B., Lu, J., and Chu, W. (2004). A complexity measure for ontology based on UML. In: Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems (FTDCS), 222-228. IEEE, Suzhou, China.
[52] Karout, R. and Awasthi, A. (2017). Improving software quality using six sigma DMAIC-based approach: A case study. Business Process Management Journal, 23(4), 842-856.
[53] Keschenau, M. (2004, October). Reverse engineering of UML specifications from Java programs. In: Companion to the 19th Annual ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), 326-327. ACM, Vancouver, BC, Canada.
[54] Kitchenham, B., Pickard, L. and Pfleeger, S.L. (1995) Case studies for method and tool evaluation, IEEE Software, 12(4), 52-62.
[55] Kollmann, R., Selonen, P., Stroulia, E., Systa, T., & Zundorf, A. (2002). A study on the current state of the art in tool-supported UML-based static reverse engineering. In: Proceedings of the Ninth Working Conference on Reverse Engineering, 22-32. IEEE, Richmond, VA, USA.
[56] Korshunova, E., Petkovic, M., Van Den Brand, M. G. J., & Mousavi, M. R. (2006). CPP2XMI: Reverse engineering of UML class, sequence, and activity diagrams from C++ source code. In: The 13th Working Conference on Reverse Engineering, 297-298. IEEE, Benevento, Italy.
[57] Kraut, R.E., & Streeter, L.A. (1995). Coordination in software development. Communications of the ACM, 38(3), 69-82. ACM, University of Vermont, USA.
[58] Lacerda, G., Petrillo, F., Pimenta, M., & Guéhéneuc, Y. G. (2020). Code smells and refactoring: A tertiary systematic review of challenges and observations. Journal of Systems and Software, 167, 110610.
[59] Lange, C.F.J., Chaudron, M.R.V., & Muskens, J. (2006). In practice: UML software architecture and design description. IEEE Software, 23(2), 40-46.
[60] Lanza, M., & Marinescu, R. (2010). Object-oriented metrics in practice: Using software metrics to characterize, evaluate, and improve the design of object-oriented systems (1st edition). Berlin, Germany: Springer.
[61] Li, W., & Shatnawi, R. (2007). An empirical study of the bad smells and class error probability in the post-release object-oriented system evolution. Journal of Systems and Software, 80, 1120-1128.
[62] Lias Webdesign. (2018). ModelGoon. Retrieved from: http://www.modelgoon.org/ (2021.06.01).
[63] Liu, Z. (2011). A method of SVM with normalization in intrusion detection. Procedia Environmental Sciences, 11, 256-262.
[64] Lu, T., Liu, C., Duan, H., & Zeng, Q. (2020). Mining component-based software behavioral models using dynamic analysis. IEEE Access, 8, 68883-68894.
[65] Maedche, A. and Staab, S. (2001). Ontology learning for the semantic web. IEEE Intelligent systems, 16(2), 72-79.
[66] Malhotra, R. (2016). Empirical research in software engineering: Concepts, analysis, and applications. Book, Chapman and Hall/CRC Press.
[67] Marinescu, R. (2001). Detecting design flaws via metrics in object-oriented systems. In: The 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS), 173-182. IEEE, Santa Barbara, CA, USA.
[68] Martinez-Cruz, C., Blanco, I. J., & Vila, M. A. (2012). Ontologies versus relational databases: are they so different? A comparison. Artificial Intelligence Review, 38(4), 271-290
[69] McCabe, T. J. (1976). A complexity measure. IEEE Transactions on Software Engineering, 4, 308-320.
[70] McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C Recommendation, 10(10), 2004.
[71] Mens, T., & Tourwé, T. (2004). A survey of software refactoring. IEEE Transactions on Software Engineering, 30(2), 126-139.
[72] MetricsReloaded (2021). MetricsReloaded - plugin for IntelliJ IDEs | JetBrains. Retrieved from: https://plugins.jetbrains.com/plugin/93-metricsreloaded (2021.06.01).
[73] Microsoft. (2018). Microsoft Visual Studio: Microsoft. Retrieved from: https://www.visualstudio.com/ (2021.06.01).
[74] Milovančević, M., Marinović, J. S., Nikolić, J., Kitić, A., Shariati, M., Trung, N. T., & Khorami, M. (2019). UML diagrams for dynamical monitoring of rail vehicles. Physica A: Statistical Mechanics and its Applications, 531, 121169.
[75] Misra, S., Adewumi, A., Fernández-Sanz, L., & Damaševičius, R. (2018). A suite of object oriented cognitive complexity metrics. IEEE Access, 6, 8782-8796.
[76] Misra, S., & Akman, I. (2008). Weighted class complexity: A measure of complexity for object oriented system. Journal of Information Science and Engineering, 24, 1689-1708.
[77] Mkaouer, M. W., Kessentini, M., Cinnéide, M. Ó., Hayashi, S., & Deb, K. (2017). A robust multi-objective approach to balance severity and importance of refactoring opportunities. Empirical Software Engineering, 22(2), 894-927.
[78] Moha, N., Guéhéneuc, Y. G., Le Meur, A. F., & Duchien, L. (2008, March). A domain analysis to specify design defects and generate detection algorithms. In: International Conference on Fundamental Approaches to Software Engineering, 276-291. Springer, Berlin, Heidelberg.
[79] Moha, N., Guéhéneuc, Y., Meur, A.L., Duchien, L., & Tiberghien, A. (2009). From a domain analysis to the specification and detection of code and design smells. Formal Aspects of Computing, 22, 345-361.
[80] Moha, N., Guéhéneuc, Y., Duchien, L., & Meur, A.L. (2010). DECOR: A method for the specification and detection of code and design smells. IEEE Transactions on Software Engineering, 36, 20-36.
[81] Noy, N.F. and McGuinness, D.L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report. Stanford, CA, USA.
[82] Nuñez-Varela, A. S., Pérez-Gonzalez, H. G., Martínez-Perez, F. E., & Soubervielle-Montalvo, C. (2017). Source code metrics: A systematic mapping study. Journal of Systems and Software, 128, 164-197.
[83] ObjectAid. (2018). the ObjectAid UML explorer for Eclipse. Retrieved from: http://objectaid.com (2021.06.01).
[84] Osman, H. and Chaudron, M.R. (2018). Correctness and completeness of CASE tools in reverse engineeringsource code into UML model. GSTF Journal on Computing (JoC), 2(1), 193-201.
[85] Ozkaya, M., & Erata, F. (2020). A survey on the practical use of UML for different software architecture viewpoints. Information and Software Technology, 121, 106275.
[86] Palomba, F., Bavota, G., Penta, M.D., Fasano, F., Oliveto, R., & Lucia, A. (2017). On the diffuseness and the impact on maintainability of code smells: A large scale empirical investigation. Empirical Software Engineering, 23, 1188-1221.
[87] Piveta, E. K. (2009). Improving the search for refactoring opportunities on object-oriented and aspect-oriented software. Thesis, Universidade Federal do Rio Grande do Sul. Instituto de Informática. Programa de Pós-Graduação em Computação.
[88] Qamar, N., & Malik, A.A. (2020). Impact of design patterns on software complexity and size. Mehran University Research Journal of Engineering and Technology, 39(2), 342-352, Jamshoro, Pakistan.
[89] Radjenović, D., Heričko, M., Torkar, R., & Živkovič, A. (2013). Software fault prediction metrics: A systematic literature review. Information and Software Technology, 55(8), 1397-1418.
[90] Raibulet, C., Fontana, F. A., & Zanoni, M. (2017). Model-driven reverse engineering approaches: A systematic literature review. IEEE Access, 5, 14516-14542.
[91] Rakić, G., Tóth, M., & Budimac, Z. (2020). Toward recursion aware complexity metrics. Information and Software Technology, 118, 106203.
[92] Rekoff, M.G. (1985). On reverse engineering. IEEE Transactions on System, Man, and Cybernetics, 2(4), 244-252.
[93] Rhmann, W., Pandey, B., Ansari, G., & Pandey, D.K. (2020). Software fault prediction based on change metrics using hybrid algorithms: An empirical study. Journal of King Saud University-Computer and Information Sciences, 32(4), 419-424.
[94] Sarkar, M. K., Chatterjee, T., & Mukherjee, D. (2013). Reverse engineering: An analysis of static behaviors of object oriented programs by extracting UML class diagram. International Journal of Advanced Computer Research, 3(3), 135.
[95] Sheldon, F. and Chung, H. (2006). Measuring the complexity of class diagrams in reverse engineering. Journal of Software Maintenance and Evolution: Research and Practice, 18, 333–350.
[96] Shobowale, K. O. (2020). Ontology in medicine as a database management system. Ontology‐Based Information Retrieval for Healthcare Systems, 69-90.
[97] Shull, F., Singer, J., & Sjøberg, D. I. (2007). Guide to advanced empirical software engineering. Book, Springer Science & Business Medi.
[98] Snae, C., & Brueckner, M. (2008). Personal health assistance service expert system (PHASES). International Journal of Biological and Medical Sciences, 1(2), 109-112.
[99] Sultana, K. Z., Anu, V., & Chong, T. Y. (2020). Using software metrics for predicting vulnerable classes and methods in Java projects: A machine learning approach. Journal of Software: Evolution and Process, 33(3), e2303.
[100] Systa, T., Yu, P., & Muller, H. (2000). Analyzing Java software by combining metrics and program visualization. In: The Fourth European Conference on Software Maintenance and Reengineering, 199-208. IEEE, Zurich, Switzerland.
[101] Tokuda, L., & Batory, D. (2001). Evolving object-oriented designs with refactorings. Automated Software Engineering, 8(1), 89-120.
[102] Trochim, W. M., & Donnelly, J. (2001). Research methods knowledge base (Vol. 2). Book, Atomic Dog Pub.
[103] Tsantalis, N., Mansouri, M., Eshkevari, L., Mazinanian, D., Dig, D. (2018). Accurate and efficient refactoring detection in commit history. In: The 40th International Conference on Software Engineering (ICSE), 483-494. IEEE/ACM, Gothenburg, Sweden.
[104] Vale, G., Fernandes, E., & Figueiredo, E. (2018). On the proposal and evaluation of a benchmark-based threshold derivation method. Software Quality Journal, 27, 275-306.
[105] Wongthongtham, P., Chang, E., Dillon, T., & Sommerville, I. (2009). Development of a software engineering ontology for multisite software development. IEEE Transactions on Knowledge and Data Engineering, 21(8), 1205-1217.
[106] Zhao, L., & Hayes, J. (2006). Predicting classes in need of refactoring: An application of static metrics. In: Proceedings of the 2nd International Participative Research Laboratory for Multimedia and Multilingual Information Systems Evaluation (PROMISE) Workshop, Philadelphia, Pennsylvania, USA.
[107] Zuse, H. (1991). Software complexity: Measures and methods (Vol. 4). Book, Walter de Gruyter GmbH & Co KG.

指導教授

陳仲儼(Chung-Yang Chen)

審核日期

2021-7-5

推文