Coupling (computer programming)
About: Coupling (computer programming) is a research topic. Over the lifetime, 1280 publications have been published within this topic receiving 14195 citations.
Papers published on a yearly basis
TL;DR: This work explains how MIMO and diversity antennas with mutual coupling can be analyzed by classical embedded element patterns that can be computed by standard computer codes and how the radiation efficiency, diversity gain, correlation, and channel capacity can be measured in a reverberation chamber.
Abstract: MIMO systems are characterized by their maximum available capacity, which is reduced if there is correlation between the signals on different channels. The correlation is primarily caused by mutual coupling between the elements of the antenna arrays on both the receiving and transmitting sides. Similarly, diversity antennas can be characterized by a diversity gain that also is affected by mutual coupling between the antennas. We explain how such MIMO and diversity antennas with mutual coupling can be analyzed by classical embedded element patterns that can be computed by standard computer codes. In the MIMO example under investigation, the mutual coupling reduces both correlation, which increases the capacity, and radiation efficiency, which decreases it, and the combined effect is a net capacity reduction. We also explain how the radiation efficiency, diversity gain, correlation, and channel capacity can be measured in a reverberation chamber. The measurements show good agreement with simulations.
•27 Dec 2006
TL;DR: How coupling can be defined and precisely measured based on dynamic analysis of systems is described and some dynamic coupling measures are significant indicators of change proneness and that they complement existing coupling measures based on static analysis.
Abstract: The relationships between coupling and external quality factors of object-oriented software have been studied extensively for the past few years. For example, several studies have identified clear empirical relationships between class-level coupling and class fault-proneness. A common way to define and measure coupling is through structural properties and static code analysis. However, because of polymorphism, dynamic binding, and the common presence of unused ("dead") code in commercial software, the resulting coupling measures are imprecise as they do not perfectly reflect the actual coupling taking place among classes at runtime. For example, when using static analysis to measure coupling, it is difficult and sometimes impossible to determine what actual methods can be invoked from a client class if those methods are overridden in the subclasses of the server classes. Coupling measurement has traditionally been performed using static code analysis, because most of the existing work was done on nonobject oriented code and because dynamic code analysis is more expensive and complex to perform. For modern software systems, however, this focus on static analysis can be problematic because although dynamic binding existed before the advent of object-orientation, its usage has increased significantly in the last decade. We describe how coupling can be defined and precisely measured based on dynamic analysis of systems. We refer to this type of coupling as dynamic coupling. An empirical evaluation of the proposed dynamic coupling measures is reported in which we study the relationship of these measures with the change proneness of classes. Data from maintenance releases of a large Java system are used for this purpose. Preliminary results suggest that some dynamic coupling measures are significant indicators of change proneness and that they complement existing coupling measures based on static analysis.
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