An empirical validation of software cost estimation models
Communications of the ACM
The role of programming language in estimating software development costs
Journal of Management Information Systems
Effort estimation in a system development project
Journal of Systems Management
Software engineering metrics and models
Software engineering metrics and models
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Proceedings of the 24th International Conference on Software Engineering
Improving Size Estimates Using Historical Data
IEEE Software
Guest Editors' Introduction: Global Software Development
IEEE Software
AI Tools for Software Development Effort Estimation
SEEP '96 Proceedings of the 1996 International Conference on Software Engineering: Education and Practice (SE:EP '96)
The software maintenance project effort estimation model based on function points
Journal of Software Maintenance: Research and Practice
COCOMO-Based Effort Estimation for Iterative and Incremental Software Development
Software Quality Control
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Software effort models should be assessed via leave-one-out validation
Journal of Systems and Software
On the value of outlier elimination on software effort estimation research
Empirical Software Engineering
Information and Software Technology
Information and Software Technology
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Companies usually have limited amount of data for effort estimation. Machine learning methods have been preferred over parametric models due to their flexibility to calibrate the model for the available data. On the other hand, as machine learning methods become more complex they need more data to learn from. Therefore the challenge is to increase the performance of the algorithm when there is limited data. In this research we used a relatively complex machine learning algorithm, neural networks, and showed that stable and accurate estimations are achievable with an ensemble using associative memory. Our experimental results revealed that our proposed algorithm (ENNA) achieves on the average PRED(25) = 36.4 which is a significant increase compared to Neural Network (NN) PRED(25) = 8.