Missing data imputation techniques for software effort estimation: a study of recent issues and challenges

Abstract

Software effort estimation is one the critical aspects of software engineering. It revolves around predicting the required efforts needed to complete a software task. However, any estimation technique or model relies on an input data in which it defines and predicts future values. Missing data and values within such data is a common occurrence in the software development industry and thus it leads to inaccurate predictions or misleading results. Thus, Missing Data is an important aspect of effort estimation models that is required to be addressed. However, Missing Data is not without its gaps and issues. This review aims at elaborating the recent issues and gaps that exist within the missing data and software effort estimation field. This may allow future researchers to get a better grasp and understanding of the inner workings of Missing Data and the methods through which these challenges can be addressed.