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Methods for Statistical and Visual Comparison of Imputation Methods for Missing Data in Software Cost Estimation
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Author(s): Lefteris Angelis (Aristotle University of Thessaloniki, Greece), Panagiotis Sentas (Aristotle University of Thessaloniki, Greece), Nikolaos Mittas (Aristotle University of Thessaloniki, Greece)and Panagiota Chatzipetrou (Aristotle University of Thessaloniki, Greece)
Copyright: 2011
Pages: 21
Source title:
Modern Software Engineering Concepts and Practices: Advanced Approaches
Source Author(s)/Editor(s): Ali H. Dogru (Middle East Technical University, Turkey)and Veli Biçer (FZI Research Center for Information Technology, Germany)
DOI: 10.4018/978-1-60960-215-4.ch009
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Abstract
Software Cost Estimation is a critical phase in the development of a software project, and over the years has become an emerging research area. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. The purpose of this chapter is to show how a combination of modern statistical and computational techniques can be used to compare the effect of missing data techniques on the accuracy of cost estimation. Specifically, a recently proposed missing data technique, the multinomial logistic regression, is evaluated and compared with four older methods: listwise deletion, mean imputation, expectation maximization and regression imputation with respect to their effect on the prediction accuracy of a least squares regression cost model. The evaluation is based on various expressions of the prediction error and the comparisons are conducted using statistical tests, resampling techniques and a visualization tool, the regression error characteristic curves.
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