A Stochastic Approach Based on Rational Decision-Making for Analyzing Software Engineering Project Status

This study presents a novel approach to project status prediction in software engineering, based on unobservable states of decision-making processes, utilizing Hidden Markov Models (HMMs). By establishing HMM structures and leveraging the Rational Decision Making model (RDM), we encoded underlying project conditions; observed project data from a software engineering organization were utilized to estimate model parameters via the Baum-Welch algorithm. The developed HMMs, four project-specific models, were subsequently tested with empirical data, demonstrating their predictive potential. However, a generalized, aggregated model did not show any sufficient accuracy. Model development and experiments were made in Python. Our approach presents preliminary work and a pathway for understanding and forecasting project dynamics in software development environments.