Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A model for measuring information system size
MIS Quarterly
Recent advances in software estimation techniques
ICSE '92 Proceedings of the 14th international conference on Software engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Proceedings of the Conference on The Future of Software Engineering
Artificial Intelligence: A Guide to Intelligent Systems
Artificial Intelligence: A Guide to Intelligent Systems
Improving Size Estimates Using Historical Data
IEEE Software
Software Measurement: Uncertainty and Causal Modeling
IEEE Software
Learning How to Improve Effort Estimation in Small Software Development Companies
COMPSAC '00 24th International Computer Software and Applications Conference
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
A threshold varying bisection method for cost sensitive learning in neural networks
Expert Systems with Applications: An International Journal
Programmer and analyst time/cost estimation
MIS Quarterly
A review of studies on expert estimation of software development effort
Journal of Systems and Software
IEEE Transactions on Neural Networks
ACM SIGSOFT Software Engineering Notes
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
We propose a probabilistic neural network (PNN) approach for simultaneously estimating values of software development parameter (either software size or software effort) and probability that the actual value of the parameter will be less than its estimated value. Using real-world software engineering datasets and V-fold sampling, we compare the PNN approach with the chi-squared automatic interaction detection (CHAID) approach and find that the PNN approach performs similar to the CHAID, but provides superior probability estimates. We also show how the method of odds likelihood ratios can be used to combine the PNN forecasted values with subjective managerial beliefs to improve probability estimates.