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Title: Evaluating prediction systems in software project estimation
Authors: Shepperd, M
MacDonell, S
Keywords: Software engineering;Prediction system;Empirical validation;Randomisation techniques
Issue Date: 2012
Publisher: Elsevier
Citation: Information and Software Technology, 54(8): 820 - 827, Aug 2012
Abstract: Context: Software engineering has a problem in that when we empirically evaluate competing prediction systems we obtain conflicting results. Objective: To reduce the inconsistency amongst validation study results and provide a more formal foundation to interpret results with a particular focus on continuous prediction systems. Method: A new framework is proposed for evaluating competing prediction systems based upon (1) an unbiased statistic, Standardised Accuracy, (2) testing the result likelihood relative to the baseline technique of random ‘predictions’, that is guessing, and (3) calculation of effect sizes. Results: Previously published empirical evaluations of prediction systems are re-examined and the original conclusions shown to be unsafe. Additionally, even the strongest results are shown to have no more than a medium effect size relative to random guessing. Conclusions: Biased accuracy statistics such as MMRE are deprecated. By contrast this new empirical validation framework leads to meaningful results. Such steps will assist in performing future meta-analyses and in providing more robust and usable recommendations to practitioners.
Description: This is the Pre-print version of the Article - Copyright @ 2012 Elsevier
ISSN: 0950-5849
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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