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Conceptual Model Clustering: A Relator-Centric Approach
Supplementary Materials

This repository contains supplementary materials for the research paper (under review):

Guizzardi, G., Sales, T. P., Almeida, J. P. A., & Poels, G. (2021). Conceptual Model Clustering: A Relator-Centric Approach. Software and Systems Modeling.

In the paper, we report on an empirical experiment in which conceptual modeling experts expressed their preferences for different modularization strategies.

In this repository, you will find the following material related to this experiment:

  • The dataset containing the answers to the survey (answers.csv)
  • The survey forms used in the experiment

Authors

  • Giancarlo Guizzardi
    Conceptual and Cognitive Modeling Research Group (CORE), Free University of Bozen-Bolzano, Bolzano, Italy
  • Tiago Prince Sales
    Conceptual and Cognitive Modeling Research Group (CORE), Free University of Bozen-Bolzano, Bolzano, Italy
  • João Paulo A. Almeida
    Ontology & Conceptual Modeling Research Group (NEMO), Federal University of Espírito Santo, Vitória, Brazil
  • Geert Poels
    UGent Business Informatics Research Group, Ghent University, Ghent, Belgium

Survey forms

Dataset

The columns in answers.csv are interpreted as follows:

  • Subject: A number that identifies the subject who answered the survey

  • Survey: A string that identifies which of the 3 variants of the survey the subject answered. Possible values are:

    • ABC
    • IJK
    • XYZ
  • Experience in conceptual modeling: A string that identifies how much experience the subject declared to have in conceptual modeling. Possible values:

    • 0 to 4 years
    • 4 to 8 years
    • More than 8 years
  • Knowledge of conceptual modeling languages: A string containing a list of conceptual modeling languages the subject declared to have knowledge of. May contain, but is not limited to, the following values:

    • Unified Modeling Language (UML)
    • OntoUML
    • (Extended) Entity Relationship (ER)
    • Object-Role Modeling (ORM)
    • SysML
  • Expertise: A string indentifying how much of an expert in conceptual modeling the subject considered herself/himself to be. Possible values:

    • Novice
    • Intermediate
    • Expert
  • Current position: A string identifying the current professional position held by the subject. May contain, but is not limited to, one of the following values:

    • BSc-level student
    • MSc-level student
    • PhD-level student
    • Post-doctoral researcher
    • Professor
    • Business analyst / architect
    • System analyst / expert
    • Software engineer
    • Other IT staff position
    • Other business function
    • Middle or senior level manager
  • Relator-centric (Rank): A number identifying how the subject ranked the Relator-centric modularization approach. Possible values:

    • 1 (best)
    • 2
    • 3 (worst)
  • Akoka (Rank): A number identifying how the subject ranked Akoka and Comyn-Wattiau’s modularization approach. Possible values:

    • 1 (best)
    • 2
    • 3 (worst)
  • Castano (Rank): A number identifying how the subject ranked Castano et al.’s modularization approach. Possible values:

    • 1 (best)
    • 2
    • 3 (worst)
  • Relator-centric Cohesion: A number corresponding to how much the subejct agreed with the assertion that the modules generated by the Relator-centric modularization approach are highly cohesive. Possible values:

    • 0: Disagree entirely
    • 1: Somewhat disagree
    • 2: Neither agree or disagree
    • 3: Somewhat agree
    • 4: Agree entirely
  • Akoka Cohesion: A number corresponding to how much the subejct agreed with the assertion that the modules generated by Akoka and Comyn-Wattiau’s modularization approach are highly cohesive. Possible values:

    • 0: Disagree entirely
    • 1: Somewhat disagree
    • 2: Neither agree or disagree
    • 3: Somewhat agree
    • 4: Agree entirely
  • Castano Cohesion: A number corresponding to how much the subejct agreed with the assertion that the modules generated by Castano et al.’s modularization approach are highly cohesive. Possible values:

    • 0: Disagree entirely
    • 1: Somewhat disagree
    • 2: Neither agree or disagree
    • 3: Somewhat agree
    • 4: Agree entirely
  • Ranking Motivation: An optional textual explanation given by the subjects for their ranking of the modularization approaches.

PS: This dataset is completely anonymized, as we did not collect any information that could be used uncover the identity of the participants.

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