scholarly journals Dynamic coupling measurement for object-oriented software

2004 ◽  
Vol 30 (8) ◽  
pp. 491-506 ◽  
Author(s):  
E. Arisholm ◽  
L.C. Briand ◽  
A. Foyen
Keyword(s):  
Object Oriented ◽  
Dynamic Coupling

scholarly journals Comparing Static and Dynamic Weighted Software Coupling Metrics

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 24 ◽  
Author(s):  
Henning Schnoor ◽  
Wilhelm Hasselbring
Keyword(s):  
Software Quality ◽  
Object Oriented ◽  
Software System ◽  
Dynamic Coupling ◽  
Commercial Software ◽  
Dynamic Case ◽  
Compiled Code ◽  
Software Coupling ◽  
Dynamic Metrics ◽  
Production Dynamic

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.

scholarly journals A new technique for understanding large-scale software systems

2020 ◽  
Vol 12 (1) ◽  
pp. 34-39
Author(s):  
Thamer Al-Rousan ◽  
Hasan Abualese
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Object Oriented ◽  
Software Systems ◽  
Specific Class ◽  
Dynamic Coupling ◽  
Novel Technique ◽  
Execution Trace ◽  
Class Dynamic ◽  
A New Technique

Comprehending a huge execution trace is not a straightforward task due to the size of data to be processed. Detecting and removing utilities are useful to facilitate the understanding of software and decrease the complexity and size of the execution trace. The goal of this study is to develop a novel technique to minimize the complexity and the size of traces by detecting and removing utilities from the execution trace of object-oriented software. Two novel utility detection class metrics were suggested to decide the degree that a specific class can be counted as a utility class. Dynamic coupling analysis forms the basis for the proposed technique to address object-oriented features. The technique presented in this study has been tested by two case studies to evaluate the effectiveness of the proposed technique. The results from the case studies show the usefulness and effectiveness of our technique.

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