在軟體反向工程中以本體論為基礎建立一套UML品質評核之方法－以耦合度為例

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徐子涵(HSU, TZU-HAN) 查詢紙本館藏

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論文名稱

在軟體反向工程中以本體論為基礎建立一套UML品質評核之方法－以耦合度為例
(Design and Implementation of Ontology-based Evaluation System for Quality of UML Generated by Software Reverse Engineering: Focusing on Coupling metrics)

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摘要(中)

隨著資訊科技的日新月異，軟體市場逐漸擴增，而為了在軟體市場中競爭，除了開發時程要符合客戶需求外，軟體品質也是一項至關重要的要素。倘若客戶要求的時程過短，開發人員會選擇較彈性或敏捷的開發方式，進而省略了事前的系統設計文件，直接進行軟體實作。然而，系統的設計文件最主要的兩個用途是在軟體實作階段確保人員撰寫出品質好的系統，以及在維護階段幫助人員理解系統內容。而目前最常見的做法是利用UML反向工程工具於系統完成後產生與系統相對應的UML圖形，但是這樣的做法卻帶來了潛在的品質問題。若能有一套機制可於UML圖形產出後，進一步探討該圖形之品質問題，則能有效協助人員修正系統品質。
因此，本研究針對UML圖形中的結構模型圖之品質進行探討，並以耦合度的靜態分析與動態分析作為品質評核之標準。此外本研究結合了本體論方法來建立品質評核之知識架構，使領域知識能隨著時間與地點的不同進行修正。結合上述兩點，本研究實作了一套Web-based的UML圖形的品質評核系統－Ontology-based Reverse Engineering Diagram Quality Inference System，並透過一個Java專案來展示該系統之功能與效益，最後以專家訪談的方式驗證本系統。

摘要(英)

Not only satisfying clients’ need in time but also delivering quality software is the major requirement in software market along with the development of IT industry and the boom in software developing environment. Some projects might choose a more agile or flexible approach of development in order to fulfill the requirement with less time by skipping the process of creating documents for the project beforehand. However, the two main purposes of foregoing documents are assuring the quality of software and helping personnel to understand how system works. One of the most common ways to create UML diagram for agile projects after software development is to use UML reverse engineering tools. But UML diagrams generated by UML reverse engineering tools have potential quality problems.
This study tried to establish a model containing multiple measures for evaluating the quality of structural UML diagram created by UML reverse engineering tools. These measures are coupling metrics measure by static analysis and dynamic analysis and using ontology to help for inferring quality rule. In addition, this study propose an evaluation system－Ontology-based Reverse Engineering Diagram Quality Inference System(OntREDQ), which aims at evaluating quality of UML diagram using foregoing model. The result of evaluation would be demonstrated by a Java project and the effectiveness of the model would be verified and discussed by professional experts.

關鍵字(中)

★ 軟體反向工程
★ UML結構模型圖
★ 軟體品質
★ 本體論
★ 品質推論

關鍵字(英)

★ Software Reverse Engineering
★ Structural UML Diagram
★ Software Quality
★ Ontology
★ Quality Inference

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章　緒論 1
1-1 研究背景與動機 1
1-2 研究問題與目的 2
1-3 研究範圍與限制 5
1-4 研究架構 6
第二章　文獻探討 8
2-1 軟體反向工程 8
2-1-1 UML反向工程研究與工具 8
2-1-2 UML反向工程之品質 9
2-2 本體論 10
2-2-1 本體語言 11
2-2-2 軟體工程本體 13
2-3 耦合度 13
2-3-1靜態耦合度 14
2-3-2動態耦合度 15
2-4 未使用耦合 16
第三章　系統設計 18
3-1 系統架構 18
3-2 資料擷取 19
3-2-1 靜態資料 19
3-2-2 動態資料 20
3-3 本體建置 22
3-3-1 建立概念 24
3-3-2 建立規則 28
3-4 品質度量與結果展示 30
3-4-1 靜態耦合度量 30
3-4-2 未使用耦合度量 32
3-4-3 釋例與說明 33
第四章　系統實作與展示 39
4-1 系統操作－專案資料擷取 39
4-2 系統操作－品質評核結果 44
第五章　系統成果與討論 52
5-1 系統驗證 52
5-2 專家訪談 56
5-2-1 認知有用性 57
5-2-2 認知易用性 60
5-2-3 訪談彙總 61
第六章　結論 62
6-1 研究貢獻 62
6-2 研究限制與未來發展 63
參考文獻 64

參考文獻

[1] 楊芝瑩（民106）。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。
[2] Abrahamsson, P., Salo, O., Ronkainen, J., & Warsta, J. (2017). Agile software development methods: Review and analysis. arXiv preprint arXiv:1709.08439.
[3] Al Balushi, T. H., Sampaio, P. R. F., Dabhi, D., & Loucopoulos, P. (2007). ElicitO: a quality ontology-guided NFR elicitation tool. Paper presented at the International Working Conference on Requirements Engineering: Foundation for Software Quality.
[4] Amir, M., Khan, K., Khan, A., & Khan, M. (2013). An appraisal of agile software development process. International Journal of Advanced Science & Technology, 58(56), 20.
[5] Antoniou, G., & Van Harmelen, F. (2004). A semantic web primer: MIT press.
[6] Arisholm, E., Briand, L. C., & Foyen, A. (2004). Dynamic coupling measurement for object-oriented software. IEEE Transactions on software engineering, 30(8), 491-506.
[7] Bagheri, E., & Gasevic, D. (2011). Assessing the maintainability of software product line feature models using structural metrics. Software Quality Journal, 19(3), 579-612.
[8] Bajnaid, N. O., Benlamri, R., Pakstas, A., & Salekzamankhani, S. (2016). An Ontological Approach to Model Software Quality Assurance Knowledge Domain. Lecture Notes on Software Engineering, 4(3), 193.
[9] Bansiya, J., & Davis, C. G. (2002). A hierarchical model for object-oriented design quality assessment. IEEE Transactions on software engineering, 28(1), 4-17.
[10] Basili, V. R., Briand, L. C., & Melo, W. L. (1996). A validation of object-oriented design metrics as quality indicators. IEEE Transactions on software engineering, 22(10), 751-761.
[11] Basili, V. R., & Caldiera, G. (1995). Improve software quality by reusing knowledge and experience. MIT Sloan Management Review, 37(1), 55.
[12] Baskerville, R., & Dulipovici, A. (2006). The theoretical foundations of knowledge management. Knowledge Management Research & Practice, 4(2), 83-105.
[13] Bavota, G., Dit, B., Oliveto, R., Di Penta, M., Poshyvanyk, D., & De Lucia, A. (2013). An empirical study on the developers′ perception of software coupling. Paper presented at the Proceedings of the 2013 International Conference on Software Engineering.
[14] Borst, W. N., & Borst, W. (1997). Construction of engineering ontologies for knowledge sharing and reuse.
[15] Briand, L. C., Daly, J. W., & Wust, J. K. (1999). A unified framework for coupling measurement in object-oriented systems. IEEE Transactions on software engineering, 25(1), 91-121.
[16] 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.
[17] Briand, L. C., Wüst, J., Daly, J. W., & Porter, D. V. (2000). Exploring the relationships between design measures and software quality in object-oriented systems. Journal of systems and software, 51(3), 245-273.
[18] Canfora, G., Di Penta, M., & Cerulo, L. (2011). Achievements and challenges in software reverse engineering. Communications of the ACM, 54(4), 142-151.
[19] Chi, Y.-L. (2009). Ontology-based curriculum content sequencing system with semantic rules. Expert Systems with Applications, 36(4), 7838-7847.
[20] Chidamber, S. R., Darcy, D. P., & Kemerer, C. F. (1998). Managerial use of metrics for object-oriented software: An exploratory analysis. IEEE Transactions on software engineering, 24(8), 629-639.
[21] Chidamber, S. R., & Kemerer, C. F. (1991). Towards a metrics suite for object oriented design (Vol. 26): ACM.
[22] Chidamber, S. R., & Kemerer, C. F. (1994). A metrics suite for object oriented design. IEEE Transactions on software engineering, 20(6), 476-493.
[23] Chikofsky, E. J., & Cross, J. H. (1990). Reverse engineering and design recovery: A taxonomy. IEEE software, 7(1), 13-17.
[24] Chong, C. Y., & Lee, S. P. (2015). Analyzing maintainability and reliability of object-oriented software using weighted complex network. Journal of systems and software, 110, 28-53.
[25] Ciancarini, P., Nuzzolese, A. G., Presutti, V., & Russo, D. An Ontology of Software Quality Relational Factors from Banking Systems.
[26] Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.
[27] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
[28] de Almeida Biolchini, J. C., Mian, P. 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] Dennis, A., Wixom, B. H., & Tegarden, D. (2015). Systems analysis and design: An object-oriented approach with UML: John wiley & sons.
[30] Di Lucca, G. A., Fasolino, A. R., & Tramontana, P. (2004). Reverse engineering Web applications: the WARE approach. Journal of Software: Evolution and Process, 16(1‐2), 71-101.
[31] Dzidek, W. J., Arisholm, E., & Briand, L. C. (2008). A realistic empirical evaluation of the costs and benefits of UML in software maintenance. IEEE Transactions on software engineering, 34(3), 407-432.
[32] e Abreu, F. B., & Melo, W. (1996). Evaluating the impact of object-oriented design on software quality. Paper presented at the Software Metrics Symposium, 1996., Proceedings of the 3rd International.
[33] e Abreu, F. B., Pereira, G., & Sousa, P. (2000). A coupling-guided cluster analysis approach to reengineer the modularity of object-oriented systems. Paper presented at the Software Maintenance and Reengineering, 2000. Proceedings of the Fourth European.
[34] Eclipse. (2018). Eclipse: The Eclipse Foundation. Retrieved from https://www.eclipse.org/
[35] Fensel, D. (2001). Ontologies Ontologies (pp. 11-18): Springer.
[36] 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.
[37] Fowler, M. (2001). Reducing coupling. IEEE software, 18(4), 102.
[38] Fowler, M., & Beck, K. (1999). Refactoring: improving the design of existing code: Addison-Wesley Professional.
[39] Franke, D., Elsemann, C., Kowalewski, S., & Weise, C. (2011). Reverse engineering of mobile application lifecycles. Paper presented at the Reverse Engineering (WCRE), 2011 18th Working Conference on.
[40] Gahalaut, A. K., & Khandnor, P. (2010). Reverse engineering: an essence for software re-engineering and program analysis. International Journal of Engineering Science and Technology, 2(06), 2296-2303.
[41] Geetika, R., & Singh, P. (2014). Dynamic coupling metrics for object oriented software systems: a survey. ACM SIGSOFT Software Engineering Notes, 39(2), 1-8.
[42] Genero, M., Fernández-Saez, A. M., Nelson, H. J., Poels, G., & Piattini, M. (2011). Research review: a systematic literature review on the quality of UML models. Journal of Database Management (JDM), 22(3), 46-70.
[43] Genero, M., Piattini, M., Manso, E., & Cantone, G. (2003). Building UML class diagram maintainability prediction models based on early metrics. Paper presented at the Software Metrics Symposium, 2003. Proceedings. Ninth International.
[44] Gethers, M., Dit, B., Kagdi, H., & Poshyvanyk, D. (2012). Integrated impact analysis for managing software changes. Paper presented at the Proceedings of the 34th international conference on software engineering.
[45] Glass, R. L. (2002). Facts and fallacies of software engineering: Addison-Wesley Professional.
[46] Gronback, R. C. (2003). Software remodeling: Improving design and implementation quality. Borland White Paper, www. borland. com/products/white_papers/pdf/tgr_softwareremodeling. pdf.
[47] Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition, 5(2), 199-220.
[48] Guarino, N. (1998). Formal ontology in information systems: Proceedings of the first international conference (FOIS′98), June 6-8, Trento, Italy (Vol. 46): IOS press.
[49] Gui, G., & Scott, P. D. (2007). Ranking reusability of software components using coupling metrics. Journal of systems and software, 80(9), 1450-1459.
[50] Gui, G., & Scott, P. D. (2009). Measuring Software Component Reusability by Coupling and Cohesion Metrics. JCP, 4(9), 797-805.
[51] Gupta, V. (2011). Validation of dynamic coupling metrics for object-oriented software. ACM SIGSOFT Software Engineering Notes, 36(5), 1-3.
[52] Gyimothy, T., Ferenc, R., & Siket, I. (2005). Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on software engineering, 31(10), 897-910.
[53] Happel, H.-J., & Seedorf, S. (2006). Applications of ontologies in software engineering. Paper presented at the Proc. of Workshop on Sematic Web Enabled Software Engineering"(SWESE) on the ISWC.
[54] Hassoun, Y., Johnson, R., & Counsell, S. (2004). A dynamic runtime coupling metric for meta-level architectures. Paper presented at the Software Maintenance and Reengineering, 2004. CSMR 2004. Proceedings. Eighth European Conference on.
[55] Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172.
[56] Horrocks, I., Patel-Schneider, P. F., Boley, H., Tabet, S., Grosof, B., & Dean, M. (2004). SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission, 21, 79.
[57] Husein, S., & Oxley, A. (2009). A coupling and cohesion metrics suite for object-oriented software. Paper presented at the Computer Technology and Development, 2009. ICCTD′09. International Conference on.
[58] Janicijevic, I., Krsmanovic, M., Zivkovic, N., & Lazarevic, S. (2016). Software quality improvement: a model based on managing factors impacting software quality. Software Quality Journal, 24(2), 247-270.
[59] Kayed, A., Hirzalla, N., Samhan, A. A., & Alfayoumi, M. (2009). Towards an ontology for software product quality attributes. Paper presented at the Internet and Web Applications and Services, 2009. ICIW′09. Fourth International Conference on.
[60] Keschenau, M. (2004). Reverse engineering of UML specifications from Java programs. Paper presented at the Companion to the 19th annual ACM SIGPLAN conference on Object-oriented programming systems, languages, and applications.
[61] 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. Paper presented at the Reverse Engineering, 2002. Proceedings. Ninth Working Conference on.
[62] Kumar, L., & Rath, S. K. (2017). Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept. International Journal of System Assurance Engineering and Management, 8(2), 1487-1502.
[63] Lange, C. F., & Chaudron, M. R. (2005). Managing model quality in UML-based software development. Paper presented at the Software Technology and Engineering Practice, 2005. 13th IEEE International Workshop on.
[64] Lange, C. F., Chaudron, M. R., & Muskens, J. (2006). In practice: UML software architecture and design description. IEEE software, 23(2), 40-46.
[65] Li, W., & Henry, S. (1993). Object-oriented metrics that predict maintainability. Journal of systems and software, 23(2), 111-122.
[66] Lias Webdesign. (2018). ModelGoon. Retrieved from http://www.modelgoon.org/
[67] Liu, H., Ma, Z., Shao, W., & Niu, Z. (2012). Schedule of bad smell detection and resolution: A new way to save effort. IEEE Transactions on software engineering, 38(1), 220-235.
[68] Lu, Y., Panetto, H., Ni, Y., & Gu, X. (2013). Ontology alignment for networked enterprise information system interoperability in supply chain environment. International Journal of Computer Integrated Manufacturing, 26(1-2), 140-151.
[69] Maedche, A., Motik, B., Stojanovic, L., Studer, R., & Volz, R. (2003). Ontologies for enterprise knowledge management. IEEE Intelligent systems, 18(2), 26-33.
[70] Maedche, A., & Staab, S. (2001). Ontology learning for the semantic web. IEEE Intelligent systems, 16(2), 72-79.
[71] Malhotra, R., & Chug, A. (2014). Application of group method of data handling model for software maintainability prediction using object oriented systems. International Journal of System Assurance Engineering and Management, 5(2), 165-173.
[72] 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.
[73] McGuinness, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C recommendation, 10(10), 2004.
[74] Microsoft. (2018). Microsoft Visual Studio: Microsoft. Retrieved from https://www.visualstudio.com/
[75] Mitchell, Á., & Power, J. F. (2004). An empirical investigation into the dimensions of run-time coupling in Java programs. Paper presented at the Proceedings of the 3rd international symposium on Principles and practice of programming in Java.
[76] Morente-Molinera, J. A., Wikström, R., Herrera-Viedma, E., & Carlsson, C. (2016). A linguistic mobile decision support system based on fuzzy ontology to facilitate knowledge mobilization. Decision Support Systems, 81, 66-75.
[77] Morris, M. G., & Dillon, A. (1997). How user perceptions influence software use. IEEE software, 14(4), 58-65.
[78] Motogna, S., Ciuciu, I., Serban, C., & Vescan, A. (2015). Improving software quality using an ontology-based approach. Paper presented at the OTM Confederated International Conferences" On the Move to Meaningful Internet Systems".
[79] Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology: Stanford knowledge systems laboratory technical report KSL-01-05 and Stanford medical informatics technical report SMI-2001-0880, Stanford, CA.
[80] O’connor, M., Knublauch, H., Tu, S., Grosof, B., Dean, M., Grosso, W., & Musen, M. (2005). Supporting rule system interoperability on the semantic web with SWRL. Paper presented at the International Semantic Web Conference.
[81] ObjectAid. (2018). The ObjectAid UML Explorer for Eclipse: ObjectAid, LLC. Retrieved from http://objectaid.com
[82] Olague, H. M., Etzkorn, L. H., Gholston, S., & Quattlebaum, S. (2007). Empirical validation of three software metrics suites to predict fault-proneness of object-oriented classes developed using highly iterative or agile software development processes. IEEE Transactions on software engineering, 33(6), 402-419.
[83] Oracle. (2018): Oracle, Corp. Retrieved from http://plugins.netbeans.org/plugin/55435/easyuml
[84] Osman, H., & Chaudron, M. R. (2012). Correctness and Completeness of CASE Tools in Reverse EngineeringSource Code into UML Model. GSTF Journal on Computing (JoC), 2(1).
[85] Petre, M. (2013). UML in practice. Paper presented at the Proceedings of the 2013 International Conference on Software Engineering.
[86] Pfleeger, S. L., & Atlee, J. M. (2001). Software engineering: theory and practice: Pearson Education India.
[87] Poshyvanyk, D., Marcus, A., Ferenc, R., & Gyimóthy, T. (2009). Using information retrieval based coupling measures for impact analysis. Empirical software engineering, 14(1), 5-32.
[88] Razmerita, L. (2011). An ontology-based framework for modeling user behavior—A case study in knowledge management. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 41(4), 772-783.
[89] Rekoff, M. G. (1985). On reverse engineering. IEEE Transactions on systems, man, and cybernetics(2), 244-252.
[90] Revelle, M., Gethers, M., & Poshyvanyk, D. (2011). Using structural and textual information to capture feature coupling in object-oriented software. Empirical software engineering, 16(6), 773-811.
[91] Sadaf, S., Athar, A., & Azam, F. (2016). Evaluation of FED-CASE-A Tool to Convert Class Diagram into Structural Coding. Paper presented at the Computer, Consumer and Control (IS3C), 2016 International Symposium on.
[92] 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.
[93] Schwalbe, K. (2015). Information technology project management: Cengage Learning.
[94] Singh, P., & Singh, H. (2010). Class-level Dynamic Coupling Metrics for Static and Dynamic Analysis of Object-Oriented Systems. International Journal of Information and Telecommunication Technology, 1(1), 16-28.
[95] Staab, S., Studer, R., Schnurr, H.-P., & Sure, Y. (2001). Knowledge processes and ontologies. IEEE Intelligent systems, 16(1), 26-34.
[96] Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: principles and methods. Data & knowledge engineering, 25(1-2), 161-197.
[97] Sudhaman, P., & Thangavel, C. (2015). Efficiency analysis of ERP projects—software quality perspective. International Journal of Project Management, 33(4), 961-970.
[98] Systa, T., Yu, P., & Muller, H. (2000). Analyzing Java software by combining metrics and program visualization. Paper presented at the Software Maintenance and Reengineering, 2000. Proceedings of the Fourth European.
[99] Tryggeseth, E. (1997). Report from an experiment: Impact of documentation on maintenance. Empirical software engineering, 2(2), 201-207.
[100] Tufano, M., Palomba, F., Bavota, G., Oliveto, R., Di Penta, M., De Lucia, A., & Poshyvanyk, D. (2015). When and why your code starts to smell bad. Paper presented at the Software Engineering (ICSE), 2015 IEEE/ACM 37th IEEE International Conference on.
[101] Whetzel, P. L., Noy, N. F., Shah, N. H., Alexander, P. R., Nyulas, C., Tudorache, T., & Musen, M. A. (2011). BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic acids research, 39(suppl_2), W541-W545.
[102] 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.
[103] Yacoub, S. M., Ammar, H. H., & Robinson, T. (1999). Dynamic metrics for object oriented designs. Paper presented at the Software Metrics Symposium, 1999. Proceedings. Sixth International.
[104] Zhao, Y., Dong, J., & Peng, T. (2009). Ontology classification for semantic-web-based software engineering. IEEE Transactions on Services Computing, 2(4), 303-317.
[105] Zimmermann, T., & Nagappan, N. (2008). Predicting defects using network analysis on dependency graphs. Paper presented at the Software Engineering, 2008. ICSE′08. ACM/IEEE 30th International Conference on.

指導教授

陳仲儼

審核日期

2018-7-10

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