Stability of user satisfaction prediction in software projects
Łukasz Radliński · Procedia Computer Science · 2020
The goal of this paper was to investigate the stability of predictions of user satisfaction using the extended version of the ISBSG dataset. The analysis involved building and training 40 models using 12 machine learning techniques. The results were analysed using a ‘win-tie-loss’ procedure based on a statistical significance. The overall best performing models were random forests. However, 19 models using ten techniques performed the best in at least one of 20 passes. High variability of data across passes caused the difficulty of predictions in some passes.