Software effort estimation plays an important role in the software development process: inaccurate estimation leads to poor\nutilization of resources and possibly to software project failure. Many software effort estimation techniques have been tried in\nan effort to develop models that generate optimal estimation accuracy, one of which is machine learning. It is crucial in machine\nlearning to use a model that will maximize accuracy and minimize uncertainty for the purposes of software effort estimation.\nHowever, the process of selecting the best algorithm for estimation is complex and expert-dependent. This paper proposes an\napproach to analyzing datasets, automatically building estimation models with various machine learning techniques, and evaluating\nand comparing their results to find the model that produces the most accurate and surest estimates for a specific dataset.\nThe proposed approach to automated model selection combines the Bayesian information criterion, correlation coefficients, and\nPRED measures.
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