Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework

Bastian Tenbergen, Marian Daun

2024

Abstract

Model-driven development has established itself as one of the core practices in software engineering. Increases in quality demands paired with shorter times to market and increased mission-criticality of software systems have sensitized software engineering practitioners to make use of not only formal, but also semi-formal models, particularly graphical diagrams to express the system under development in ways that facilitate collaboration, validation & verification, as well as configuration and runtime monitoring. However, what does and does not constitute a “good” model, i.e., a model that is fit for a practical purpose? While some model quality frameworks exist, the trouble with most of these is that they often lack the ability to concretely quantify and thereby objectively differentiate a “good” from a “poor” model, i.e., models that can be easily understood by the model reader. Without being able to reliably produce easily comprehensible models, training new team members during on-boarding or educating software engineering students is dramatically hindered. In this paper, we report on a research trajectory towards reliably measuring the comprehensibility of graphical diagrams.

Paper Citation

in Harvard Style

Tenbergen B. and Daun M. (2024). Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework. In Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-696-5, SciTePress, pages 498-505. DOI: 10.5220/0012684800003687

in Bibtex Style

@conference{enase24,
author={Bastian Tenbergen and Marian Daun},
title={Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework},
booktitle={Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2024},
pages={498-505},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012684800003687},
isbn={978-989-758-696-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Metrics to Estimate Model Comprehension: Towards a Reliable Quantification Framework
SN - 978-989-758-696-5
AU - Tenbergen B.
AU - Daun M.
PY - 2024
SP - 498
EP - 505
DO - 10.5220/0012684800003687
PB - SciTePress