An empirical validation of software cost estimation models
Communications of the ACM
An Evaluation of Expert Systems for Software Engineering Management
IEEE Transactions on Software Engineering
Multilayer feedforward networks are universal approximators
Neural Networks
What size net gives valid generalization?
Neural Computation
Calibrating estimation tools for software development
Software Engineering Journal
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A Comparison of Function Point Counting Techniques
IEEE Transactions on Software Engineering
Sensitivity analysis of fuzzy and neural network models
ACM SIGSOFT Software Engineering Notes
ACM SIGSOFT Software Engineering Notes
ENNA: software effort estimation using ensemble of neural networks with associative memory
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
ANN model for predicting software function point metric
ACM SIGSOFT Software Engineering Notes
Integrate the GM(1,1) and Verhulst models to predict software stage effort
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Software reusability assessment using soft computing techniques
ACM SIGSOFT Software Engineering Notes
Radial basis function neural network based approach to test oracle
ACM SIGSOFT Software Engineering Notes
A comparative study of models for predicting fault proneness in object-oriented systems
International Journal of Computer Applications in Technology
| Hi-index | 0.00 |
Software development involves a number of interrelated factors which affect development effort and productivity. Since many of these relationships are not well understood, accurate estimation of software development time and effort is a difficult problem. Most estimation models in use or proposed in the literature are based on regression techniques. This paper examines the potential of two artificial intelligence approaches i.e. artificial neural networks and case-based reasoning for creating development effort estimation models. Artificial neural networks can provide accurate estimates when there are complex relationships between variables and where the input data is distorted by high noise levels Case-based reasoning solves problems by adapting solutions for old problems similar to the current problem. This research examines both the performance of back-propagation artificial neural networks in estimating software development effort and the potential of case-based reasoning for development estimation using the same dataset. Keywords: Function points, Software development, Artificial neural networks, Case based reasoning