Farm price prediction using case-based reasoning approach-A case of broiler industry in Taiwan
Computers and Electronics in Agriculture
Software effort estimation based on optimized model tree
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Adjusted case-based software effort estimation using bees optimization algorithm
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
A replicated assessment and comparison of adaptation techniques for analogy-based effort estimation
Empirical Software Engineering
Evaluating prediction systems in software project estimation
Information and Software Technology
Alternative methods using similarities in software effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
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OBJECTIVE - the aim of this investigation is to perform an independent replication of the Jørgensen et al. study that advocates exploiting a phenomenon known as regression to the mean for software project productivity when predicting software project effort. METHOD - we used two further industrial data sets in which we compare accuracy levels with and without this adjustment. RESULTS - our results were broadly consistent with those from the Jørgensen study. Using the R2M resulted in a small increase in predictive accuracy. For one data set it was necessary to first partition it into more homogeneous subsets. Also when there was very weak correlation between predicted and actual productivity using the sample mean was the least bad strategy. CONCLUSIONS - we believe that independent validation of results is an important activity. Specifically our results add further support for the R2M approach in that there is a small, but positive, effect upon prediction accuracy. By combining results from both studies we observe a consistency across all 7 data sets.