Computational Intelligence for Estimating Software Development Effort: a Systematic Mapping Study

Software development effort estimation (SDEE) is critical for predicting the required resource investment. Since the late 2000s, numerous studies have advocated using computational intelligence (CI) to enhance the precision of SDEE models (CI-SDEE). However, a systematic examination of empirical evidence surrounding CI-SDEE is lacking. Therefore, we conducted a meticulous and systematic literature review across four dimensions: CI technique classification, estimation fidelity, model comparative analysis, and contextual applicability. Surveying empirical studies published between 2008 and 2023, we identified 38 seminal works germane to our research objectives. Our investigation revealed five distinct CI technique families utilized in SDEE, exhibiting an overall estimation accuracy commensurate with the acceptable standards and superior to non-CI counterparts. In addition, we determined that specific CI models exhibit unique advantages and disadvantages, which make them better suited to specific estimation contexts. While CI techniques have been proven promising in advancing the field of SDEE, their industrial applications are limited, necessitating additional efforts to foster their adoption. This review provides actionable academic recommendations and operational guidelines for practitioners.