Comparing Static and Dynamic Weighted Software Coupling Metrics

Henning Schnoor,Wilhelm Hasselbring

doi:10.3390/computers9020024

Abstract

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.

Highlights

Coupling [1,2]—the number of inter-module connections in software systems—has long been identified as a software architecture quality metric for modularity [3]
We studied three different basic measurement approaches: Static coupling, unweighted dynamic coupling, and weighted dynamic coupling
As discussed in Section 6.1.4, suggest that dynamic coupling metrics complement their static counterparts: Despite the large difference, there is a statistically significant correlation. This suggests that further study of dynamic weighted coupling and its relationship with other coupling metrics is an interesting line of research

Summary

Introduction

Coupling [1,2]—the number of inter-module connections in software systems—has long been identified as a software architecture quality metric for modularity [3]. Taking coupling metrics into account during development of a software system can help to increase the system’s maintainability and understandability [4]. As a consequence, aiming for high cohesion and low coupling is accepted as a design guideline in software engineering [5]. There exists a wide range of different approaches to defining and measuring coupling. The coupling degree of a module (class or package) indicates the number of connections it has to different system modules. Many notions of coupling can be measured statically, based on either source code or compiled code

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Discussion

Conclusion