Insightful analogy-based software development effort estimation through selective classification and localization

Accurate development effort estimation is a challenging issue in the management of software projects because it can considerably affect the planning and scheduling of a software project. Over the past few years, many algorithmic and non-algorithmic methods have been proposed to estimate the development effort in the early stages of project. Due to simplicity and estimation capability, analogy-based estimation (ABE) method has been widely accepted by researchers in this area. In spite of the fact that ABE is an efficient estimation method, it suffers from the non-normality and heterogeneous nature of software project datasets. Although prior studies have strived to remedy this issue by weighting, soft computing, and clustering techniques, the estimate accuracy is still not convincing and attempts are ongoing to reach more reliable estimates. The problem is that prior ABE-based studies have not considered the nature of software projects in the estimation process. This paper aims to show the effect of selective project classification and estimation process localization on the performance of ABE. An exhaustive investigation is conducted based on different development types, organization types, and development platforms as three underlying attributes in software projects. An evaluation framework is designed to reveal the ABE performance when it is combined with the proposed classification. A real dataset that includes 448 software projects is utilized for the evaluation purposes. The promising results showed that the estimate accuracy is significantly improved and the estimation process is considerably expedited if the nature of software projects is considered in the ABE method.