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Title: | Predicting software project effort: A grey relational analysis based method |
Authors: | Song, Q Shepperd, M |
Keywords: | Software project estimation;Effort prediction;Feature subset selection;Outlier detection;Grey relational analysis |
Issue Date: | 2011 |
Publisher: | Elsevier Ltd |
Citation: | Expert Systems with Applications: An International Journal, 38(6), 7302 - 7316, 2011 |
Abstract: | The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) from grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential. |
Description: | This is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V. |
URI: | http://www.sciencedirect.com/science/article/pii/S0957417410013680 http://bura.brunel.ac.uk/handle/2438/8783 |
DOI: | http://dx.doi.org/10.1016/j.eswa.2010.12.005 |
ISSN: | 0957-4174 |
Appears in Collections: | Computer Science Dept of Computer Science Research Papers |
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