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A novel approach to automated behavioral diagram assessment using label similarity and subgraph edit distance

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The Unified Modeling Language (UML) is one of the standard languages that are used in modeling software; therefore, UML is widely taught in many universities. Generally, teachers assign students to build UML diagram designs based on a predetermined project; however, the assessment of such assignments can be challenging, and teachers may be inconsistent in assessing their students’ answers. Thus, automated UML diagram assessment becomes essential to maintaining assessment consistency. This study uses a behavioral diagram as the object of research, since it is a commonly taught UML diagram. The behavioral diagram can show a dynamic view of the software. This study proposes a new approach to automatically assessing the similarity of behavior diagrams as reliably as experts do. We divide the assessment into two portions: semantic assessment, and structural assessment. Label similarity is used to calculate semantic assessment, while subgraph edit distance is used to calculate structural assessment. The results suggest that the proposed approach is as reliable as an expert in assessing the similarity between two behavior diagrams. The observed agreement value suggests a strong agreement between the use of experts and the proposed approach.
Wydawca
Czasopismo
Rocznik
Tom
Strony
191–207
Opis fizyczny
Bibliogr. 33 poz., rys.
Twórcy
autor
  • Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Department of Electrical Engineering, Politeknik Negeri Banjarmasin, Banjarmasin, Indonesia
  • Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Information Systems, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar, Indonesia
Bibliografia
  • [1] Adamu A., Zainon W.: A Review of UML Model Retrieval Approaches,IndianJournal of Science and Technology, vol. 9(46), pp. 1–8, 2016.
  • [2] Adamu A., Zainon W.M.N.W.: Matching and retrieval of state machine diagramsfrom software repositories using Cuckoo Search Algorithm. In:2017 8th International Conference on Information Technology (ICIT), pp. 187–192, IEEE, 2017.
  • [3] Adamu A., Zainon W.M.N.W.: Multiview Similarity Assessment Technique ofUML Diagrams,Procedia Computer Science, vol. 124, pp. 311–318, 2017.
  • [4] Adamu A., Zainon W.M.N.W.: Similarity Assessment of UML Sequence Diagrams Using Dynamic Programming. In:International Visual Informatics Conference, pp. 270–278, Springer, 2017.
  • [5] Bloxham S., den Outer B., Hudson J., Price M.: Let’s stop the pretence ofconsistent marking: exploring the multiple limitations of assessment criteria,Assessment&Evaluation in Higher Education, vol. 41(3), pp. 466–481, 2016.
  • [6] Buijs S., Heerkens J.L.T., Ampe B., Delezie E., Rodenburg T.B., Tuyttens F.A.M: Assessing keel bone damage in laying hens by palpation: effects of assessor experience on accuracy, inter-rater agreement and intra-rater consistency,Poultry Science, vol. 98(2), pp. 514–521, 2019.
  • [7] Castro L.J.G., Berlanga R., Garcia A.: In the pursuit of a semantic similaritymetric based on UMLS annotations for articles in PubMed Central Open Access,Journal of Biomedical Informatics, vol. 57, pp. 204–218, 2015.
  • [8] Chonoles M.J.:OCUP 2 Certification Guide: Preparing for the OMG CertifiedUML 2.5 Professional 2 Foundation Exam, Morgan Kaufmann, 2017.
  • [9] Daller ́E., Bougleux S., Ga ̈uz`ere B., Brun L.: Approximate Graph Edit Distanceby Several Local Searches in Parallel. In:7th International Conference on PatternRecognition Applications and Methods, 2018.
  • [10] Fauzan R., Siahaan D.O., Rochimah S., Triandini E.: Class Diagram Similar-ity Measurement: A Different Approach. In:2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), pp. 215–219, IEEE, 2018.
  • [11] Fauzan R., Siahaan D.O., Rochimah S., Triandini E.: A Different Approach onAutomated Use Case Diagram Semantic Assessment,International Journal ofIntelligent Engineering and Systems, vol. 14(1), pp. 496–505, 2021.
  • [12] Fellbaum C.: WordNet. In:Theory and Applications of Ontology: ComputerApplications, pp. 231–243, Springer, 2010.
  • [13] Feng Y., Bagheri E., Ensan F., Jovanovic J.: The state of the art in semanticrelatedness: a framework for comparison,The Knowledge Engineering Review,vol. 32, 2017.
  • [14] Fischer A., Riesen K., Bunke H.: Improved quadratic time approximation of graph edit distance by combining Hausdorff matching and greedy assignment,Pattern Recognition Letters, vol. 87, pp. 55–62, 2017.
  • [15] Gwet K.L.: Kappa Statistic is not Satisfactory for Assessing the Extent of Agreement Between Raters,Statistical Methods for Inter-Rater Reliability Assessment,vol. 1, pp. 1–5, 2002.
  • [16] Harispe S., Ranwez S., Janaqi S., Montmain J.: Semantic similarity from natural language and ontology analysis,Synthesis Lectures on Human Language Technologies, vol. 8(1), pp. 1–254, 2015.
  • [17] Jenkins D., Simpson S., Peacock A.: Investigating the consistency and quality of EPC ratings and assessments, Energy, vol. 138, pp. 480–489, 2017.
  • [18] Jimenez A.M., Zepeda S.J.: A Comparison of Gwet’s AC1 and Kappa When Calculating Inter-Rater Reliability Coefficients in a Teacher Evaluation Context, Journal of Education Human Resources, vol. 38(3), pp. 290–300, 2020.
  • [19] Kutuzov A., Dorgham M., Oliynyk O., Biemann C., Panchenko A.: Learning Graph Embeddings from WordNet-based Similarity Measures, arXiv preprint arXiv:180805611, 2018.
  • [20] Landis J.R., Koch G.G.: The Measurement of Observer Agreement for Categorical Data, Biometrics, pp. 159–174, 1977.
  • [21] Majumder G., Pakray P., Gelbukh A., Pinto D.: Semantic Textual Similarity Methods, Tools, and Applications: A Survey, Computacion y Sistemas, vol. 20(4), pp. 647–665, 2016.
  • [22] Park W.J., Bae D.H.: A two-stage framework for UML specification matching, Information and Software Technology, vol. 53(3), pp. 230–244, 2011.
  • [23] Pressman R.S.: Software Engineering: A Practitioner’s Approach, Palgrave Macmillan, London, 2005.
  • [24] Riesen K., Bunke H.: Graph Edit Distance Novel Approximation Algorithms. In: Handbook of Pattern Recognition and Computer Vision, pp. 275–291, World Scientific, 2016.
  • [25] Riesen K., Ferrer M., Bunke H.: Approximate Graph Edit Distance in Quadratic Time, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17(2), pp. 483–494, 2015.
  • [26] Riesen K., Ferrer M., Dornberger R., Bunke H.: Greedy Graph Edit Distance. In: International Workshop on Machine Learning and Data Mining in PatternRecognition, pp. 3–16, Springer, 2015.
  • [27] Robinson W.N., Woo H.G.: Finding reusable UML sequence diagrams automatically, IEEE Software, vol. 21(5), pp. 60–67, 2004.
  • [28] 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, IEEE, 2014.
  • [29] Siahaan D.O., Desnelita Y., Gustientiedina, Sunarti: Structural and semantic similarity measurement of UML sequence diagrams. In: 2017 11th International Conference on Information & Communication Technology and System (ICTS), pp. 227–234, IEEE, 2017.
  • [30] Sommerville I.:Software engineering, Addison-Wesley, 9th ed., 2011.
  • [31] Triandini E., Fauzan R., Siahaan D.O., Rochimah S.: Sequence Diagram Similarity Measurement: A Different Approach. In: 2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 348–351, IEEE, 2019.
  • [32] Wongpakaran N., Wongpakaran T., Wedding D., Gwet K.L.: A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples,BMC Medical Re-search Methodology, vol. 13(1), p. 61, 2013.
  • [33] Yuan Z., Yan L., Ma Z.: Structural similarity measure between UML class diagrams based on UCG, Requirements Engineering, pp. 1–17, 2019.
Uwagi
PL
„Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).”
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e5b7b271-c3d8-4874-ba7e-53cf387711f2
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