Abstract
Multivariate regression models have been commonly used to estimate the software development effort to assist project planning and/or management. Since project data sets for model construction often contain missing values, we need to build a complete data set that has no missing values either by using imputation methods. However, while there are several ways to build the complete data set, it is unclear which method is the most suitable for the project data set. In this paper, using project data of 1364 cases (34% missing value rate) collected from several companies, we applied four imputation methods (k-nn method, applied CF method, Miss Forest method and Multiple Imputation method) to build regression models. Then, using project data of 160 cases (having no missing values), we evaluated the estimation performance of models after applying each imputation method. The result showed that Multiple Imputation method showed the best performance.
