An empirical validation of software cost estimation models
Communications of the ACM
Software Engineer's Reference Book
Software Engineer's Reference Book
Application of Neural Networks for Software Quality Prediction Using Object-Oriented Metrics
ICSM '03 Proceedings of the International Conference on Software Maintenance
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Software Development Effort Estimation Using Fuzzy Logic: A Case Study
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Functional networks training algorithm for statistical pattern recognition
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Improving the COCOMO model using a neuro-fuzzy approach
Applied Soft Computing
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
IEEE Transactions on Software Engineering
Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
Journal of Systems and Software
Software reliability identification using functional networks: A comparative study
Expert Systems with Applications: An International Journal
Iterative Least Squares Functional Networks Classifier
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Empirical Software Engineering
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.