Improving analogy software effort estimation using fuzzy feature subset selection algorithm
Proceedings of the 4th international workshop on Predictor models in software engineering
Updating weight values for function point counting
International Journal of Hybrid Intelligent Systems
An investigation of using neuro-fuzzy with software size estimation
WOSQ'09 Proceedings of the Seventh ICSE conference on Software quality
A novel fuzzy based approach for effort estimation in software development
ACM SIGSOFT Software Engineering Notes
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Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. In this paper, we present a soft computing framework to tackle this challenging problem. We first use a preprocessing neuro-fuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. One feature of this framework is that the architecture is inherently independent of the choice of algorithmic models or the nature of the estimation problems. By integrating neural networks, fuzzy logic and algorithmic models into one scheme, this framework has learning ability, integration capability of both expert knowledge and project data, good interpretability, and robustness to imprecise and uncertain inputs. Validation using industry project data shows that the framework produces good results when used to predict software cost.