Methods for stabilizing models across large samples of projects (with case studies on predicting defect and project health)
Suvodeep Majumder, Tianpei Xia, Rahul Krishna, Tim Menzies · 2022
Despite decades of research, Software Engineering (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).