Decision Trees Based Software Development Effort Estimation: A Systematic Mapping Study

Abstract

The decision tree (DT) represents a nonparametric estimation method that has been mostly used for both classification and regression problems. DTs were adopted for software development effort estimation (SDEE) generally for their simplicity of use and interpretation contrary to other learning methods. Nevertheless, to our self-knowledge, no systematic mapping has been devoted especially to decision trees. The aim of this study is to elaborate a systematic mapping study that classifies DTs papers in conformity with the succeeding criteria: research approach, contribution type, techniques employed in combination with DT methods besides identifying publication channels and trends. An automated search of five digital libraries was made to carry out a systematic mapping of DT studies mainly devoted to SDEE that were published in the period 1985–2017. We identify 46 relevant studies. Basically, the results revealed that most researchers focus on technique contribution type. In addition, the majority of papers deal with improving the existing DT models while few studies have proposed novel models to improve the reliability of SDEE. Furthermore, solution proposal and case study are the most frequently used approaches.