The focus of Software Development Effort Estimation (SDEE) is to precisely predict the estimation of effort and time required for successfully developing a software project. From the past few years, data-intensive applications with a huge back-end part are contributing to the overall effort of projects. Therefore, it is becoming more important to add the back-end part to the SDEE process. This paper proposes an Evolutionary Learning (EL) based hybrid artificial neuron termed as dilation-erosion perceptron (DEP) framework from the mathematical morphology (MM) having its foundation in complete lattice theory (CLT) for solving the SDEE problem. In this work, we used the DEP (CMGA) model utilizing a chaotically modified genetic algorithm (CMGA) for the construction of DEP parameters. The proposed method uses the ER diagram artifacts such as aggregation, specialization, generalization, semantic integrity constraints, etc. for calculating the SDEE of back-end part of the business software. Furthermore, the proposed method was tested over two different datasets, one is existing and the other one is a self-developed dataset. The performance of the given method is then evaluated by three popular performance metrics, exhibiting better performance of the DEP (CMGA) model for solving the SDEE problems.

In the last century, with the inception of various software development industries at around mid-1960’s, the complexities and size of the software have always been a major concern for the industries. The ad-hoc process of development has evolved into a standardized one due to the increase in the size and complexity of software projects. The standardized process of software development was further evolved to predict the overall cost required for the development before the software is actually built. To achieve the same, many cost estimation methodologies have already been successfully implemented, each with certain pros and cons. The present scenario demands even further refined and accurate predictions, which the above-said methods cease to provide. In this paper, we present a chaotically modified particle swarm optimization (CMPSO) based morphological learning approach to accurately estimate the cost incurred in the development process. The proposed approach focuses on a mathematical morphological (MM) framework based hybrid artificial neuron (also called dilation-erosion perceptron or DEP) with algebraic foundations in complete lattice theory (CLT). The proposed CMPSO-DEP model was tested on 5 well-known datasets of software projects with three popular performance metrics and the results were compared with the best existing models available in the literature.

Cloud computing is a popular computing concept that performs processing of huge volume of data using highly accessible geographically distributed resources that can be accessed by users on the basis of Pay as per Use policy. Requirements of different users may change so the amount of processing involved in such paradigm also changes. Sometimes they need huge data processing. Such highly volumetric processing results in higher computing time and cost which is not a desirable part of a good computing model. So there must be some intelligent distribution of user's work on the available resources which will result in an optimized computing environment. This paper gives a comprehensive survey on such problems and provide a detailed analysis of some best scheduling techniques from the domain of soft computing with their performance in cloud computing.