Handling of Categorical Data in Software Development Effort Estimation: A Systematic Mapping Study

Abstract

Producing reliable and accurate estimates of software effort remains a difficult task in software project management, especially at the early stages of the software life cycle where the information available is more categorical than numerical. In this paper, we conducted a systematic mapping study of papers dealing with categorical data in software development effort estimation. In total, 27 papers were identified from 1997 to January 2019. The selected studies were analyzed and classified according to eight criteria: publication channels, year of publication, research approach, contribution type, SDEE technique, Technique used to handle categorical data, types of categorical data and datasets used. The results showed that most of the selected papers investigate the use of both nominal and ordinal data. Furthermore, Euclidean distance, fuzzy logic, and fuzzy clustering techniques were the most used techniques to handle categorical data using analogy. Using regression, most papers employed ANOVA and combination of categories.