How do I find reusable models?
Pages 85 - 102
Abstract
Models play a major role in model-based development and serve as the main artifacts that stakeholders aim to achieve. As it is difficult to develop good-quality models, repositories of models start emerging for reuse purposes. Yet, these repositories face several challenges, such as model representation, scalability, heterogeneity, and how to search for models. In this paper, we aim to address the challenge of querying model repositories by proposing a generic search framework that looks for models that match the intention of the user. The framework is based on a greedy search approach using a similarity function that considers type similarity, structure similarity, and label similarity. We evaluate the framework’s efficiency on different model types: UML class diagrams, Human Know-How, and ME maps. We further compare it with existing alternatives. The evaluation indicates that the framework achieved high performance within a bounded time, and the framework can be adapted to different modeling languages for searching related, reusable models.
References
[1]
Abrahão, S., Bourdeleau, F., Cheng, B., Kokaly, S., Paige, R., Stöerrle, H., Whittle, J.: User experience for model-driven engineering: challenges and future directions. In: 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS), pp. 229– 236 (2017)
[2]
Wexler, M.N.: The who, what and why of knowledge mapping. J. Knowl. Manage. (2001)
[3]
Pandey, D., Suman, U., Ramani, A.K.: An effective requirement engineering process model for software development and requirements management. In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp. 287– 291 (2010). IEEE
[4]
Agt-Rickauer, H., Kutsche, R.-D., Sack, H.: Domore-a recommender system for domain modeling. In: the 6th International Conference on Model-Driven Engineering and Software Development, pp. 71– 82 (2018)
[5]
Robles, G., Ho-Quang, T., Hebig, R., Chaudron, M.R.V., Fernandez, M.A.: An extensive dataset of UML models in GITHUB. In: Proceedings of the 14th International Conference on Mining Software Repositories. MSR ’17, pp. 519– 522. IEEE Press, ( 2017)
[6]
Hebig, R., Quang, T.H., Chaudron, M.R.V., Robles, G., Fernandez, M.A.: The quest for open source projects that use UML: mining GITHUB. In: The ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems. MODELS ’16, pp. 173– 183 (2016)
[7]
Basciani, F., Rocco, J.D., Ruscio, D.D., Iovino, L., Pierantonio, A.: Model repositories: will they become reality?. In: the 3rd International Workshop on Model-Driven Engineering, pp. 37– 42 (2015)
[8]
Yuan, Z., Yan, L., Ma, Z.: Structural similarity measure between UML class diagrams based on UCG. Requirements Engineering, 1–17 (2019)
[9]
López, J.A.H., Cuadrado, J.S.: Mar: a structure-based search engine for models. In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pp. 57– 67 (2020)
[10]
Bragilovski, M., Makias, Y., Shamshila, M., Stern, R., Sturm, A.: Searching for class models. In: Augusto, A., Gill, A., Nurcan, S., Reinhartz-Berger, I., Schmidt, R., Zdravkovic, J. (eds.) Enterprise, Business-Process and Information Systems Modeling - 22nd International Conference, BPMDS 2021, and 26th International Conference, EMMSAD 2021, Held at CAiSE 2021, Melbourne, VIC, Australia, June 28-29, 2021, Proceedings. Lecture Notes in Business Information Processing, vol. 421, pp. 277– 292. Springer (2021)
[11]
Bragilovski, M., Makias, Y., Shamshila, M., Stern, R., Sturm, A.: Model-based knowledge searching. In: Ghose, A.K., Horkoff, J., Souza, V.E.S., Parsons, J., Evermann, J. (eds.) Conceptual Modeling - 40th International Conference, ER 2021, Virtual Event, October 18-21, 2021, Proceedings. Lecture Notes in Computer Science, vol. 13011, pp. 242– 256. Springer, (2021)
[12]
Di Rocco, J., Di Sipio, C., Di Ruscio, D., Nguyen, P.T.: A GNN-based recommender system to assist the specification of metamodels and models. In: 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), IEEE. pp. 70– 81 (2021)
[13]
Antunes, G., Bakhshandeh, M., Borbinha, J., Cardoso, J., Dadashnia, S., Di Francescomarino, C., Dragoni, M., Fettke, P., Gal, A., Ghidini, C., et al.: The process model matching contest 2015 vol. 248. Geellschaft für Informatik ( 2015)
[14]
Dumas M, García-Bañuelos L, and Dijkman RM Similarity search of business process models IEEE Data Eng. Bull. 2009 32 3 23-28
[15]
Ehrig M, Koschmider A, and Oberweis A Measuring similarity between semantic business process models APCCM 2007 7 71-80
[16]
Dijkman R, Dumas M, Van Dongen B, Käärik R, and Mendling J Similarity of business process models: metrics and evaluation Inf. Syst. 2011 36 2 498-516
[17]
Messmer, B.: Efficient graph matching algorithms for preprocessed model graphs [ph. d. thesis]. University of Bern 58 (1996)
[18]
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings 18th International Conference on Data Engineering, IEEE. pp. 117– 128 (2002)
[19]
Ferrucci D, Brown E, Chu-Carroll J, Fan J, Gondek D, Kalyanpur AA, Lally A, Murdock JW, Nyberg E, Prager J, et al. Building Watson: an overview of the Deepqa project AI Mag. 2010 31 3 59-79
[20]
Paredaens J, Peelman P, and Tanca L G-log: a graph-based query language IEEE Trans. Knowl. Data Eng. 1995 7 3 436-453
[21]
Wang, Y., Khan, A., Wu, T., Jin, J., Yan, H.: Semantic guided and response times bounded top-k similarity search over knowledge graphs. In: 36th International Conference on Data Engineering (ICDE), IEEE, pp. 445– 456 (2020)
[22]
Han, S., Zou, L., Yu, J.X., Zhao, D.: Keyword search on RDF graphs-a query graph assembly approach. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 227– 236 ( 2017)
[23]
Zou, L., Huang, R., Wang, H., Yu, J.X., He, W., Zhao, D.: Natural language question answering over RDF: a graph data driven approach. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 313– 324 (2014)
[24]
Hu, S., Zou, L., Zhang, X.: A state-transition framework to answer complex questions over knowledge base. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2098– 2108 (2018)
[25]
Pérez J, Arenas M, and Gutierrez C Semantics and complexity of sparql ACM Trans. Database Syst. (TODS) 2009 34 3 1-45
[26]
Angles R, Arenas M, Barceló P, Hogan A, Reutter J, and Vrgoč D Foundations of modern query languages for graph databases ACM Comput. Surv. (CSUR) 2017 50 5 1-40
[27]
Prud’hommeaux, E., Seaborne, A., et al.: SPARQL query language for RDF. W3C Recommendation (2008) (2017)
[28]
Francis, N., Green, A., Guagliardo, P., Libkin, L., Lindaaker, T., Marsault, V., Plantikow, S., Rydberg, M., Selmer, P., Taylor, A.: Cypher: An evolving query language for property graphs. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1433– 1445 (2018)
[29]
Rodriguez, M.A.: The gremlin graph traversal machine and language (invited talk). In: Proceedings of the 15th Symposium on Database Programming Languages, pp. 1– 10 (2015)
[30]
Robles K, Fraga A, Morato J, and Llorens J Towards an ontology-based retrieval of UML class diagrams Inf. Softw. Technol. 2012 54 1 72-86
[31]
Al-Khiaty MA-R and Ahmed M UML class diagrams: similarity aspects and matching Lect. Notes Softw. Eng. 2016 4 1 41
[32]
Nikiforova, O., Gusarovs, K., Kozacenko, L., Ahilcenoka, D., Ungurs, D.: An approach to compare UML class diagrams based on semantical features of their elements. In: the Tenth International Conference on Software Engineering Advances, pp. 147– 152 (2015)
[33]
Xing, Z., Stroulia, E.: Umldiff: an algorithm for object-oriented design differencing. In: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering, pp. 54– 65 (2005)
[34]
Salami, H.O., Ahmed, M.: Retrieving sequence diagrams using genetic algorithm. In: 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 324– 330 ( 2014). IEEE
[35]
Steinberg, D., Budinsky, F., Merks, E., Paternostro, M.: EMF: Eclipse Modeling Framework. Pearson Education (2008)
[36]
Arasu A, Cho J, Garcia-Molina H, Paepcke A, and Raghavan S Searching the web ACM Trans. Internet Technol. (TOIT) 2001 1 1 2-43
[37]
Robertson, S., Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc, (2009)
[38]
Bislimovska, B., Bozzon, A., Brambilla, M., Fraternali, P.: Graph-based search over web application model repositories. In: International Conference on Web Engineering, pp. 90– 104 (2011). Springer
[39]
Lucrédio, D., M Fortes, R.P.d., Whittle, J.: Moogle: A model search engine. In: International Conference on Model Driven Engineering Languages and Systems, pp. 296– 310 (2008) Springer
[40]
Barmpis, K., Kolovos, D.S.: Towards scalable querying of large-scale models. In: European Conference on Modelling Foundations and Applications, pp. 35– 50 (2014) Springer
[41]
Kling, W., Jouault, F., Wagelaar, D., Brambilla, M., Cabot, J.: Moscript: A DSL for querying and manipulating model repositories. In: International Conference on Software Language Engineering, pp. 180– 200 (2011) Springer
[42]
Reinhartz-Berger I Towards automatization of domain modeling Data Knowl. Eng. 2010 69 5 491-515
[43]
Klau GW A new graph-based method for pairwise global network alignment BMC Bioinf. 2009 10 1 1-9
[44]
Sturm A, Gross D, Wang J, and Yu E Means-ends based know-how mapping J. Knowl. Manag. 2017 21 454-473
[45]
Pareti, E.H. Paolo; Klein: The Human Know-How Dataset. (2014)
[46]
Bargilovski, M., Stern, R., Sturm, A.: Searching Models. https://dropbox.com/sh/2bx32q3f860shgw/AACkXilbNPQuaCkGieMeWR7Wa?dl=0 (2021)
[47]
Zimmerman DW and Zumbo BD Relative power of the Wilcoxon test, the Friedman test, and repeated-measures Anova on ranks J. Exp. Educ. 1993 62 1 75-86
[48]
Woolson, R.F.: Wilcoxon Signed-Rank Test. Wiley encyclopedia of clinical trials, pp. 1–3 (2007)
[49]
Pereira DG, Afonso A, and Medeiros FM Overview of Friedman’s test and post-hoc analysis Commun. Stat. Simul. Comput. 2015 44 10 2636-2653
[50]
McNemar Q Note on the sampling error of the difference between correlated proportions or percentages Psychometrika 1947 12 2 153-157
Recommendations
Querying large models efficiently
Context: The paradigm of Model-Driven Engineering (MDE) has emerged as a new area of software engineering that uses models to improve the productivity and reusability of software in order to achieve industrial standards. As models grow in size and ...
Querying business process models with VMQL
BMFA '13: Proceedings of the 5th ACM SIGCHI Annual International Workshop on Behaviour Modelling - Foundations and ApplicationsThe Visual Model Query Language (VMQL) has been invented with the objectives (1) to make it easier for modelers to query models effectively, and (2) to be universally applicable to all modeling languages. In previous work, we have applied VMQL to UML, ...
A homogeneous repository for collaborative MDE
IWMCP '10: Proceedings of the 1st International Workshop on Model Comparison in PracticeThe Model Driven Development (MDD) paradigm is gaining acceptance due to its ability to raise the level of abstraction and automation in the construction of software. However, current tools lack of some capabilities that are essential for MDD ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 06 April 2023
Accepted: 08 March 2023
Revision received: 06 November 2022
Received: 02 March 2022
Author Tags
Qualifiers
- Research-article
Funding Sources
- Data Science Center at Ben-Gurion University of the Negev
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in