Early Estimation in Agile Software Development Projects: A Systematic Mapping Study

Abstract

Estimating during the early stages is crucial for determining the feasibility and conducting the budgeting and planning of agile software development (ASD) projects. However, due to the characteristics of ASD and limited initial information, these estimates are often complicated and inaccurate. This study aims to systematically map the literature to identify the most used estimation techniques; the reasons for their selection; the input artifacts, predictors, and metrics associated with these techniques; as well as research gaps in early-stage estimations in ASD. This study was based on the guidelines proposed by Kitchenham for systematic literature reviews in software engineering; a review protocol was defined with research questions and criteria for the selection of empirical studies. Results show that data-driven techniques are preferred to reduce biases and inconsistencies of expert-driven techniques. Most selected studies do not mention input artifacts, and software size is the most commonly used predictor. Machine learning-based techniques use publicly available data but often contain records of old projects from before the agile movement. The study highlights the need for tools supporting estimation activities and identifies key areas for future research, such as evaluating hybrid approaches and creating datasets of recent projects with sufficient contextual information and standardized metrics.