Titlebook: Software Engineering Research, Management and Applications; Roger Lee Book 2016 Springer International Publishing Switzerland 2016 Computa

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Software Engineering Research, Management and Applications978-3-319-33903-0Series ISSN 1860-949X Series E-ISSN 1860-9503

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A Framework for Requirements Knowledge Acquisition Using UML and Conceptual Graphs,hs back to UML notations. This bidirectional transforming process enables the discovery of additional requirements and possible missing requirements so that eliciting more requirements knowledge from modelers is made possible.

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A Fuzzy Logic Utility Framework (FLUF) to Support Information Assurance,ogic for accelerating the transformation of network monitoring tool alerts to actionable knowledge, suggests process improvement that combines information assurance and cyber defender expertise for holistic computer network defense, and describes an experimental design for collecting empirical data to support the continued research in this area.

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Identification Method of Fault Level Based on Deep Learning for Open Source Software,re reliability assessment in the actual software projects. Moreover, we compare the methods to estimate the level of software faults based on the deep learning by using the fault data sets of actual software projects.

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On the Prevalence of Function Side Effects in General Purpose Open Source Software Systems,ore architectures) becomes very costly or impractical to conduct. Analysis of the historical data over a 7-year period for 10 systems shows that there is a relatively large percentage of affected functions over the lifetime of the systems although trend is flat in general, thus posing further problems for inter-procedural analysis.

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PMRF: Parameterized Matching-Ranking Framework,uation has been conducted employing the SME2 (Semantic Matchmaker Evaluation Environment) tool. The results show that the algorithms behave globally well in comparison to iSeM-logic-based and SPARQLent.

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Monitoring Target Through Satellite Images by Using Deep Convolutional Networks,rst, the target area is labelled manually to form a dataset. In the second stage a classifier based on DCNN using Keras library is well-trained. In the last stage the target is monitored in the test satellite images. The method was tested on two different application scenarios. The results show that the mothed is effective.

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