Ensemble based point and confidence interval forecasting in software engineering

• Proposed an analytical approach to estimate confidence interval (CI).
• Used simulation of truncated distribution to heuristically estimate CI.
• Built an ensemble of different approaches and tested it on different real-world datasets.

In this paper, we propose and use analytical and heuristic approaches for forecasting 95% confidence intervals (CIs) for software effort or size parameter. The analytical and heuristics forecasts are combined to create an ensemble to lower generalization error. Using ordinary least squares (OLS) and Bayesian regression models, we create three different ensembles. These three different ensembles are one maximizing maximum likelihood hypothesis (MLE), one maximizing maximum a posteriori hypothesis (MAP), a hybrid one maximizing both MLE and MAP hypotheses. Using two different performance metrics, we test the three different ensembles on real-world software engineering datasets. The results of our experiments indicated that the hybrid ensemble shows the worst performance while the MLE ensemble provides superior performance in most cases.