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Despite decades of research, SE lacks widely accepted models (that offerprecise quantitative stable predictions) about what factors most influencesoftware quality. This paper provides a promising result showing such stablemodels can be generated using a new transfer learning framework called"STABILIZER". Given a tree of recursively clustered projects (using projectmeta-data), STABILIZER promotes a model upwards if it performs best in thelower clusters (stopping when the promoted model performs worse than the modelsseen at a lower level).  The number of models found by STABILIZER is minimal: one for defectprediction (756 projects) and less than a dozen for project health (1628projects). Hence, via STABILIZER, it is possible to find a few projects whichcan be used for transfer learning and make conclusions that hold acrosshundreds of projects at a time. Further, the models produced in this manneroffer predictions that perform as well or better than the priorstate-of-the-art.  To the best of our knowledge, STABILIZER is order of magnitude faster thanthe prior state-of-the-art transfer learners which seek to find conclusionstability, and these case studies are the largest demonstration of thegeneralizability of quantitative predictions of project quality yet reported inthe SE literature.  In order to support open science, all our scripts and data are online athttps://github.com/Anonymous633671/STABILIZER.

Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)