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Gao, Xiaozhi; Sangaiah, Arun Kumar; Ramachandran, MuthuSoft Computational Approaches for Prediction and Estimation of Software Development

Published in:THESCIENTIFICWORLDJOURNAL

DOI:10.1155/2016/3905931

Published: 01/01/2016

Document VersionPublisher's PDF, also known as Version of record

Please cite the original version:Gao, X., Sangaiah, A. K., & Ramachandran, M. (2016). Soft Computational Approaches for Prediction andEstimation of Software Development. THESCIENTIFICWORLDJOURNAL, 2016, [3905931].https://doi.org/10.1155/2016/3905931

https://doi.org/10.1155/2016/3905931

https://doi.org/10.1155/2016/3905931

EditorialSoft Computational Approaches for Prediction andEstimation of Software Development

Xiao-Zhi Gao,1 Arun Kumar Sangaiah,2 and Muthu Ramachandran3

1Department of Electrical Engineering and Automation, Aalto University, 00076 Espoo, Finland2School of Computing Science and Engineering, VIT University, Vellore 632014, India3School of Computing and Creative Technologies, Leeds Beckett University, Leeds LS1 3HE, UK

Correspondence should be addressed to Xiao-Zhi Gao; [email protected]

Received 22 December 2015; Accepted 22 December 2015

Copyright © 2016 Xiao-Zhi Gao et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

During the past decades, the use of soft computationalapproaches has been extended to a large variety of softwareengineering applications. Soft computational methods, suchas fuzzy systems, neural networks, evolutionary computation,and probability models including Bayesian network andchaos theory, are currently attractive topics in the extensivesoftware engineering research problems. This special issueprovides a platform for the dissemination of knowledgeon both the empirical research and applied research inthe field of soft computing. The objective of the specialissue is to facilitate a forum to a wide spectrum of arti-cles that cover the state-of-the-art techniques and recentresults in the research of soft computational approaches.In particular, the special issue focuses on publishing thehighly technical articles describing the software developmenttopics: advanced software engineering, computational intel-ligence, and wireless sensor networks. This special issue hasreceived overwhelming responses from researchers, and ithas received a total of 28 high-quality submissions fromvarious countries around the world. All the submitted papershave been evaluated by at least three independent reviewers.However, due to focused scope of the special issue, onlya few manuscripts are published. Inevitably, hard editorialdecisions had to be made, and some high-quality articlescould not even be included. We believe that this special issuepresents cohesive information related to the applications ofsoft computing methods in software development, and it alsoprovides stimulations for future research.

In the paper entitled “An Enhanced PSO-Based Clus-tering Energy Optimization Algorithm for Wireless SensorNetwork,” C. Vimalarani et al. propose a novel Enhanced

PSO-Based Clustering Energy Optimization (EPSO-CEO)algorithm for the wireless sensor network (WSN), in whichthe clustering and clustering head selection are done byusing the Particle SwarmOptimization (PSO) algorithmwithrespect to minimizing the power consumption in the WSN.

The results obtained have been compared and evaluatedon the basis of energy efficiency.

The paper of Y. Rastegari and F. Shams entitled “Opti-mal Decomposition of Service Level Objectives into PolicyAssertions” presents a method to decompose service levelobjectives to web service policy assertions. Transformationof Web Service Choreography Description Language (WS-CDL) to Web Service Business Process Execution Language(WSBPEL) has been addressed in some related works, butneither of them considers quality aspects of transformationor run-time adaptation. In this paper, in conformity withweb services standards, the authors proposed an optimaldecompositionmethod tomake a set ofWS-policy assertions.Assertions applied to WSBPEL elements and affect theirrun-time behaviors.The decomposition method achieves thebest outcome for a performance indicator. It also guaranteesthe lowest adaptation overhead by reducing the numberof service reselections. This research considered securitiessettlement case study to prototype and evaluated the decom-position method. The results show an acceptable thresholdbetween customer satisfaction, the targeted performanceindicator through the case study, and adaptation overhead.

G. J. Eanoch and T. S. Sankar present a Neuro-FuzzyEnergy Aware Clustering Scheme (NFEACS) in their paperentitled “Development of Energy Efficient Clustering Pro-tocol in Wireless Sensor Network Using Neuro-Fuzzy

Hindawi Publishing Corporatione Scientific World JournalVolume 2016, Article ID 3905931, 2 pageshttp://dx.doi.org/10.1155/2016/3905931

2 The Scientific World Journal

Approach” to form the optimum energy aware clusters. Theproposed method consists of two part, fuzzy subsystem andneural network system, which can yield energy efficiencyin building up clusters and cluster heads in the WSN.More precisely, it uses neural network that provides effectivetraining sets related to energy and received signal strength ofall nodes in order to estimate the expected energy for tentativecluster heads. The sensor nodes with higher energy aretrained with the center location of base station for selectingthe energy aware cluster heads. Fuzzy IF-THEN mappingrules are also used so as to find the clusters and cluster heads.Experiment results show that the NFEACS performs betterthan the existing solutions.

In the paper entitled “Software Design Challenges inTime Series Prediction Systems Using Parallel Implementa-tion of Artificial Neural Networks,” the authors (N.Manikan-dan and S. Subha) develop a comprehensive approach toarchitectural design for financial prediction models. Duringthe past two decades, a large number of artificial neuralnetworks-based learningmodels have been studied tomanip-ulate with financial data, for example, prediction of the futuretrends and prices. This paper focuses on the architecturaldesign related issues for performance improvement throughvectorising the strengths of multivariate econometric timeseries models and artificial neural networks. It further pro-vides an adaptive and hybrid method for predicting theexchange rates.

In the paper entitled “Energy-Aware Multipath RoutingScheme Based on Particle Swarm Optimization in MobileAd Hoc Networks” written by Y. H. Robinson and M.Rajaram, a novel energy aware multipath routing scheme isproposed, which is based on the Particle SwarmOptimization(EMPSO) that uses Continuous Time Recurrent NeuralNetwork (CTRNN) to deal with optimization problems. TheCTRNN has been applied to find the optimal loop-free pathsfor solving the link disjoint paths inMANET. In other words,it is used as an optimum path selection technique to producea set of optimal paths between source and destination. In theCTRNN, the PSOmethod is particularly utilized for trainingthe RNN.The proposed scheme uses the reliability measures,for example, transmission cost, energy factor, and the optimaltraffic ratio between source and destination, to increase therouting performance. Therefore, the optimal loop-free pathscan be discovered using the PSO to seek better link qualitynodes in the route discovery phase suitable for the dynamictopology changing property of a MANET.

Acknowledgments

We would like to express our sincere gratitude to all the con-tributors who have submitted their high-quality manuscriptsand to the experts for their support in providing reviewcomments and suggestions on time.

Xiao-Zhi GaoArun Kumar SangaiahMuthu Ramachandran

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