Details

Autor(en) / Beteiligte

• Song, Qinbao
• Shepperd, Martin

Titel

Predicting software project effort: A grey relational analysis based method

Ist Teil von

• Expert systems with applications, 2011-06, Vol.38 (6), p.7302-7316

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2011

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• ► We propose a novel approach of using grey relational analysis (GRA) to predict software effort prediction with outlier detection and feature subset selection at an early stage of a project. ► GRA is a recently developed system engineering method based on the uncertainty of small samples. ► We 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. 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.