Visible to the public Assessing the Quality of Tabular State Machines through Metrics

TitleAssessing the Quality of Tabular State Machines through Metrics
Publication TypeConference Paper
Year of Publication2017
AuthorsOsaiweran, A., Marincic, J., Groote, J. F.
Conference Name2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)
Date Publishedjul
KeywordsAdaptation models, analytical software design tooling, ASD models, coding theory, Complexity theory, compositionality, cryptography, Metrics, Model metrics, pubcrawl, quality of models, resilience, Resiliency, security, Software, Software measurement, software metrics, software quality, tabular state machines, Unified modeling language, Variable speed drives
Abstract

Software metrics are widely used to measure the quality of software and to give an early indication of the efficiency of the development process in industry. There are many well-established frameworks for measuring the quality of source code through metrics, but limited attention has been paid to the quality of software models. In this article, we evaluate the quality of state machine models specified using the Analytical Software Design (ASD) tooling. We discuss how we applied a number of metrics to ASD models in an industrial setting and report about results and lessons learned while collecting these metrics. Furthermore, we recommend some quality limits for each metric and validate them on models developed in a number of industrial projects.

URLhttp://ieeexplore.ieee.org/document/8009946/
DOI10.1109/QRS.2017.52
Citation Keyosaiweran_assessing_2017