Empirical extension of a classification framework for addressing consistency in model based development

  • Authors:

  • Ludwik Kuzniarz;Lefteris Angelis

  • Affiliations:

  • School of Computing Blekinge Institute of Technology, Soft Center, 372 25 Ronneby, Sweden;Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece

  • Venue:
  • Information and Software Technology
  • Year:
  • 2011

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Abstract

Context: Consistency constitutes an important aspect in practical realization of modeling ideas in the process of software development and in the related research which is diverse. A classification framework has been developed, in order to aid the model based software construction by categorizing research problems related to consistency. However, the framework does not include information on the importance of classification elements. Objective: The aim was to extend the classification framework with information about the relative importance of the elements constituting the classification. The research question was how to express and obtain this information. Method: A survey was conducted on a sample of 24 stakeholders from academia and industry, with different roles, who answered a quantitative questionnaire. Specifically, the respondents prioritized perspectives and issues using an extended hierarchical voting scheme based on the hundred dollar test. The numerical data obtained were first weighted and normalized and then they were analyzed by descriptive statistics and bar charts. Results: The detailed analysis of the data revealed the relative importance of consistency perspectives and issues under different views, allowing for the desired extension of the classification framework with empirical information. The most highly valued issues come from the pragmatics perspective. These issues are the most important for tool builders and practitioners from industry, while for the responders from academia theory group some issues from the concepts perspective are equally important. Conclusion: The method of using empirical data from a hierarchical cumulative voting scheme for extending existing research classification framework is useful for including information regarding the importance of the classification elements.