Ensemble based point and confidence interval forecasting in software engineering

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Abstract

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.

Original languageEnglish (US)
Pages (from-to)9441-9448
Number of pages8
JournalExpert Systems with Applications
Volume42
Issue number24
DOIs
StatePublished - Dec 30 2015

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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