An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Software development cost estimation approaches – A survey
Annals of Software Engineering
An Investigation of Analysis Techniques for Software Datasets
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Using Simulation to Evaluate Prediction Techniques
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
What Should You Optimize When Building an Estimation Model?
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Finding the Right Data for Software Cost Modeling
IEEE Software
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Modeling the relationship between software effort and size using deming regression
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A controlled experiment in assessing and estimating software maintenance tasks
Information and Software Technology
Overcoming the challenges in cost estimation for distributed software projects
Proceedings of the 34th International Conference on Software Engineering
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Building cost estimation models is often considered a search problem in which the solver should return an optimal solution satisfying an objective function. This solution also needs to meet certain constraints. For example, a solution for the estimates coefficients of COCOMO models must be non-negative. In this research, we introduce a constrained regression technique that uses objective functions and constraints to estimate the coefficients of the COCOMO models. To access the performance of the proposed technique, we run a cross-validation procedure and compare the prediction accuracy from different approaches such as least squares, stepwise, Lasso, and Ridge regression. Our result suggests that the regression model that minimizes the sum of relative errors and imposes non-negative coefficients is a favorable technique for calibrating the COCOMO model parameters.