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
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
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
Feature subset selection can improve software cost estimation accuracy
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
Finding the Right Data for Software Cost Modeling
IEEE Software
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
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
A principled evaluation of ensembles of learning machines for software effort estimation
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Empirical Software Engineering
The impact of parameter tuning on software effort estimation using learning machines
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
Software effort estimation as a multiobjective learning problem
ACM Transactions on Software Engineering and Methodology (TOSEM) - Testing, debugging, and error handling, formal methods, lifecycle concerns, evolution and maintenance
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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 paper, we use a relatively complex machine learning algorithm, neural networks, and show that stable and accurate estimations are achievable with an ensemble using associative memory. Our experimental results show that our proposed algorithm (ENNA) produces significantly better results than neural network (NN) in terms of accuracy and robustness. We also analyze the effect of feature subset selection on ENNA's estimation performance in a wrapper framework. We show that the proposed ENNA algorithm that use the features selected by the wrapper does not perform worse than those that use all available features. Therefore, measuring only company specific key factors is sufficient to obtain accurate and robust estimates about software cost estimation using ENNA.