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A Data-Interoperability Aware Software Architecture

Making heterogeneous data sources homogeneous manually and off-line can become a high time-consuming task. This paper presents a software architecture that extends the standardized-based architectures for heterogeneous sensors with components to also support devices and data that are not compliant with standards. The defined architecture is based on Internet of Things (IoT) layered architectures that establish perception, network, middleware, application, and business as main layers. To define the architecture, an architectural framework was used; this framework supports the identification of non-compliant data, providing then a different processing path. This proposed architecture covers a wide spectrum of data interoperability addressing the IoT challenge of «Interoperability and Standardization». The implemented solution proved that the processing time between data acquisition and the feeding of analysis algorithms can be reduced from 100% to approximately to 1% with systems based on the proposed architecture compared with those that manage data manually and off-line.

Design Patterns for Software Evolution Requirements

The software Engineering term known as Software Evolution can be understood in two senses. First, as the changes that software experiences over its develop-ment cycle, second, as changes that software goes through in its lifetime. In both cases, software architectures should lead, support and ease any software modifications, reconfiguration or adaptation to a changing environment. At moment, it is widely acknowledged that design and architectural patterns must be used for carrying out any software development focused on quality. We present here the analysis of several design and architectural patterns for sustaining software systems evolution according to two complementary perspectives, one connected with maintainability and the other with dynamicity of any software design.

On the Effectiveness, Efficiency and Perceived Utility of Architecture Evaluation Methods: A Replication Study

In this paper we describe the results of a replication study for comparing the effectiveness, efficiency and perceived utility of the quality-driven product architecture derivation and improvement method (QuaDAI), an architecture derivation and evaluation method that we presented in recent works, as opposed to the Architecture Tradeoff Analysis Method (ATAM), a well-known architectural evaluation method used in industry. The results of the original experiment (conducted with undergraduate students) showed that QuaDAI was found to be more efficient and was perceived as easier to use than ATAM. However, although QuaDAI performed better than ATAM, we could not confirm the other variables, as the differences between both methods were not statistically significant. Therefore the goal of the replication was to verify these findings with a group of more experienced students. In the replication study QuaDAI also performed better than ATAM, but as opposed to the original study, all the variables proved to be statistically significant.