Project productivity evaluation in early software effort estimation

Abstract The productivity factor has long been a key driver to estimate effort from Use Case Points (UCP) size measure, especially when historical dataset is absent. But, no one questions: Does productivity still matter when historical data are also available? To facilitate answering this question, the present paper studies the role of productivity from 2 perspectives. First, does learning productivity from historical data lead to better accuracy than using fixed productivity ratios? Second, what is the impact of ignoring productivity when estimating the effort from UCP? Five different models that use productivity factor have been used under different experimental settings and compared with some regression models that use only UCP size metrics. We found that dynamically learning and adjusting productivity from historical data are more efficient than using fixed productivity values. Moreover, using UCP size variables to estimate effort tends to be more accurate than using productivity and UCP variables. We also did not find any significant improvement when using UCP adjustment factors for measuring productivity. Finally, we conclude that the productivity factor is a good driver to generate effort estimate from UCP in the presence and absence of historical datasets. But using UCP size variables alone for predicting effort is more accurate than using productivity.