Software cost estimation is the prediction of development effort and calendar time required to develop any software project. It is considered to be the very fundamental task for successful execution of an on-going project as well as budgetary requirements of futuristic projects. As accuracy in software cost estimation is very hard because of the availability of vague information at the time of inception of the software project, it prompted many researchers to explore in this domain from past decades. Their pioneer works suggest a bulk of techniques for this purpose. However, because of the availability of large number of estimation techniques, it becomes hard for any software practitioners to select an appropriate one. To help the industry practitioners in these situations, a novel analogy-centered model based on differential evolution exploration process is proposed in this research study. The proposed model has been assessed on 676 projects from 5 different data sets and the results achieved are significantly better when compared with other benchmark analogy-based estimation studies. Furthermore, being the very less computational cost of the proposed model, it is suggested that the proposed model be considered as the preliminary stage of any analogy-based software estimation technique.
Software cost estimation is the forecast of development effort and time needed to develop a software project. Estimating software cost is endlessly proving to be a difficult problem and thus catches the attention of many researchers. Recently, the usage of meta-heuristic techniques for software cost estimation is increasingly growing. In this paper, we are proposing a technique consisting of functional link artificial neural network model and particle swarm optimisation algorithm as its training algorithm. Functional link artificial neural network is a high order feedforward artificial neural network consisting of an input layer and an output layer. It reduces the computational complexity and has got the fast learning ability. Particle swarm optimisation does optimisation by iteratively improving a candidate solution. The proposed model has been evaluated on promising datasets using magnitude of relative error and its median as a measure of performance index to simply weigh the obtained quality of estimation.
It is always preferable for any estimation model to be inclusive as accuracy in estimation models inherently lie with their inclusiveness. Software cost estimation is the prediction of development effort and time required to develop a software project and being predictive in nature, it demands for inclusiveness, which will accordingly bring the accuracy in it. In this study, a generic model for software cost estimation using an input selection procedure is proposed. The proposed model brings inclusiveness into the already available data mining techniques of software cost estimation by sensitively choosing a subset of highly relevant project attributes and ignoring the less relevant ones. In this article, a diverse set of data mining techniques for software cost estimation are considered. All these techniques are experimented on five data sets before and after passed through the proposed procedure. The obtained results showed that newly generated techniques after being passed through the proposed procedure offer accurate results up in the way of efficiency in software cost estimation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.