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Open AccessArticle

Comparing Static and Dynamic Weighted Software Coupling Metrics

Software Engineering Group, Kiel University, 24098 Kiel, Germany
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This paper is an extended version of our paper published in ICIST 2019, 10–12 October 2019, Vilnius, Lithuania.
Computers 2020, 9(2), 24; https://doi.org/10.3390/computers9020024
Received: 20 February 2020 / Revised: 27 March 2020 / Accepted: 28 March 2020 / Published: 30 March 2020
Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses. View Full-Text
Keywords: software metrics; monitoring; dynamic/static analysis software metrics; monitoring; dynamic/static analysis
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MDPI and ACS Style

Schnoor, H.; Hasselbring, W. Comparing Static and Dynamic Weighted Software Coupling Metrics. Computers 2020, 9, 24. https://doi.org/10.3390/computers9020024

AMA Style

Schnoor H, Hasselbring W. Comparing Static and Dynamic Weighted Software Coupling Metrics. Computers. 2020; 9(2):24. https://doi.org/10.3390/computers9020024

Chicago/Turabian Style

Schnoor, Henning; Hasselbring, Wilhelm. 2020. "Comparing Static and Dynamic Weighted Software Coupling Metrics" Computers 9, no. 2: 24. https://doi.org/10.3390/computers9020024

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