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Software Quality Journal

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01-12-2008

Imputation techniques for multivariate missingness in software measurement data

Authors: Taghi M. Khoshgoftaar, Jason Van Hulse

Published in: Software Quality Journal | Issue 4/2008

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Abstract

The problem of missing values in software measurement data used in empirical analysis has led to the proposal of numerous potential solutions. Imputation procedures, for example, have been proposed to ‘fill-in’ the missing values with plausible alternatives. We present a comprehensive study of imputation techniques using real-world software measurement datasets. Two different datasets with dramatically different properties were utilized in this study, with the injection of missing values according to three different missingness mechanisms (MCAR, MAR, and NI). We consider the occurrence of missing values in multiple attributes, and compare three procedures, Bayesian multiple imputation, k Nearest Neighbor imputation, and Mean imputation. We also examine the relationship between noise in the dataset and the performance of the imputation techniques, which has not been addressed previously. Our comprehensive experiments demonstrate conclusively that Bayesian multiple imputation is an extremely effective imputation technique.

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Title: Imputation techniques for multivariate missingness in software measurement data
Authors: Taghi M. Khoshgoftaar
Jason Van Hulse
Publication date: 01-12-2008
Publisher: Springer US
Published in: Software Quality Journal / Issue 4/2008
Print ISSN: 0963-9314
Electronic ISSN: 1573-1367
DOI: https://doi.org/10.1007/s11219-008-9054-7

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