Cost estimation of software intensive projects: a survey of current practices
ICSE '91 Proceedings of the 13th international conference on Software engineering
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Safe and Simple Software Cost Analysis
IEEE Software
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Simple software cost analysis: safe or unsafe?
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
Regularities in learning defect predictors
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
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Despite the widespread availability of software effort estimation models (e.g. COCOMO [2], Price-S [12], SEER-SEM [13], SLIM [14]), most managers still estimate new projects by extrapolating from old projects [3, 5, 7]. In this delta method, the cost of the next project is the cost of the last project multiplied by some factors modeling the difference between old and new projects [2].Delta estimation is simple, fast, and best of all, can take full advantage of local costing information. However delta estimation fails when the experience base (the old projects) can not be extrapolated to the new projects. Previously [10], we have shown that for a set of NASA projects, delta estimation would usually fail since most of the features and coefficients of the learned model vary wildly across sub-samples of the training data. In that prior work, no solution was offered for this problem.Here, we offer a solution and report the results of experiment with feature subset selection (FSS) and extrapolation. FSS methods are usually assessed via the mean change in model performance. However, as shown below, FSS can significantly reduce the variance as well. Hence, FSS should be routinely used in cost estimation.Our results should stop the trend in the effort modeling community of continually adding to the number of features in a model in order to improve estimation performance. Here we show that there are benefits in intelligently subtracting model features.