How to GENERALize Across Many Software Projects? (with case studies on Predicting Defect and Project Health)

Despite decades of research, SE lacks widely accepted models (that offer precise quantitative predictions) about what factors most influence software quality. This paper provides a "good news" result that such general models can be generated using a new transfer learning framework called "GENERAL". Given a tree of recursively clustered projects (using project meta-data), GENERAL 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 GENERAL is minimal: one for defect prediction (756 projects) and less than a dozen for project health (1628 projects). Hence, via GENERAL, it is possible to 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 prior state-of-the-art. To the best of our knowledge, this is the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature.