By Lazaros Iliadis, Michael Papazoglou, Klaus Pohl
This ebook constitutes the completely refereed complaints of 5 foreign workshops held in Thessaloniki, Greece, along side the twenty sixth foreign convention on complicated info structures Engineering, CAiSE 2014, in June 2014.
The 24 complete and 8 brief papers have been rigorously chosen from sixty three submissions.
The 5 workshops have been the 1st overseas Workshop on complex likelihood and facts in details platforms (APSIS), the 1st overseas Workshop on Advances in companies layout in accordance with the concept of potential, the second one overseas Workshop on Cognitive features of knowledge structures Engineering (COGNISE), the 3rd Workshop on New iteration firm and company Innovation structures (NGEBIS), and the 4th overseas Workshop on info structures safety Engineering (WISSE).
Read Online or Download Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece, June 16-20, 2014. Proceedings PDF
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Extra resources for Advanced Information Systems Engineering Workshops: CAiSE 2014 International Workshops, Thessaloniki, Greece, June 16-20, 2014. Proceedings
Vasa et al.  studied the evolution of classes and interfaces in open-source Java software. They analyzed the evolution based on static metrics of released versions of the systems. e. the average size of classes does not change much between released versions. This work also analyzes which types of classes change more than average and concludes that large, complex classes change more. This work does not use time series and makes no attempt to model time-dependency. In  Vasa presented an extended and more detailed version of his research.
A solution to these concerns and suggestions would be the development of a hybrid methodology, consisting of the MT strategy and a feature selection technique as a replacement for the OA-S/N ratio method.  have proposed an approach that uses binary particle swarm optimization for the feature selection problem. In this study, we propose the use of a Genetic Algorithm (GA) for optimizing the procedure of selecting the most useful variables. In addition, we proceed further in the application of the GA by formulating a GA-based reﬁnement of the training set as well.
Since our data does not have any periodic eﬀect, again, hold-out sets with length of 3 are justiﬁable. e. how our results can be generalized. Our sample set of open source Java projects are quite diverse with respect to application domains, number of revisions, number of elements etc. The process described in this paper is independent of a speciﬁc programming language, though. It can be easily applied and used to analyze the evolution of projects which are coded in a diﬀerent programming language.