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