The Maximum Entropy (MaxEnt) method is used to evaluate the Inter User Interference (IUI) probability density function in a Direct Sequence Spread Spectrum Multiple Access (DS/SSMA) system. This distribution is frequently assumed to be Gaussian distributed and is commonly known as the Gaussian Assumption (GA). By calculating the discrimination information (relative entropy) between the IUI-distribution, as inferred via the MaxEnt method, and a Gaussian distribution with equal second moments the Gaussian Assumption is quantified for the Nakagami-m faded channel. By altering the parameter m of the Nakagami-m distribution, the degree of fading can be varied and therefore the influence of fading on the Gaussian Assumption can be thoroughly investigated.
Context: Many software architectural decisions are group decisions rather than decisions made by individuals. Consensus in a group of decision makers increases the acceptance of a decision among decision makers and their confidence in that decision. Furthermore, going through the process of reaching consensus means that decision makers understand better the decision (including the decision topic, decision options, rationales, and potential outcomes). Little guidance exists on how to increase consensus in group architectural decision making.Objective: We evaluate how a newly proposed process (named GADGET) helps architects increase consensus when making group architectural decisions. Specifically, we investigate how well GADGET increases consensus in group architectural decision making, by understanding its practical applicability, and by comparing GADGET against group architectural decision making without using any prescribed approach.Method: We conducted two empirical studies. First, we conducted an exploratory case study to understand the practical applicability of GADGET in industry. We investigated whether there is a need to increase consensus, the effort and benefits of GADGET, and potential improvements for GADGET. Second, we conducted an experiment with 113 students from three universities to compare GADGET against group architectural decision making without using any prescribed approach.Results: GADGET helps decision makers increase their consensus, captures knowledge on architectural decisions, clarifies the different points of view of different decision makers on the decision, and increases the focus of the group discussions about a decision. From the experiment, we obtained causal evidence that GADGET increases consensus better than group architectural decision making without using any prescribed approach.Conclusions: There is a need to increase consensus in group architectural decisions. GADGET helps inexperienced architects increase consensus in group architectural decision making, and provides additional benefits, such as capturing rationale of decisions. Future work is needed to understand and improve other aspects of group architectural decision making.
Use-Case Responsibility-Driven Analysis and Design (UR-DAD) is a service-oriented software analysis and design methodology. It is used by requirements engineers to develop technology-neutral, semi-formal platform-independent models (PIM) within the OMG's MDA. In the past, URDAD models were denoted in UML. However, that was tedious and error-prone. The resulting models were often of rather poor quality. In this paper we introduce and discuss a new Domain-Specific Language (DSL) for UR-DAD. Its meta model is consistent and satisfiable. We show that URDAD DSL specifications are simpler and allow for more complete service contract specifications than their corresponding UML expressions. They also enable traceability and test case generation.
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