  Foundation models (e.g., CodeBERT, GraphCodeBERT, CodeT5) work well for many
software engineering tasks. These models are pre-trained (using
self-supervision) with billions of code tokens, and then fine-tuned with
hundreds of thousands of labeled examples, typically drawn from many projects.
However, software phenomena can be very project-specific. Vocabulary, and other
phenomena vary substantially with each project. Thus, training on
project-specific data, and testing on the same project, is a promising idea.
This hypothesis has to be evaluated carefully, e.g., in a time-series setting,
to prevent training-test leakage. We compare several models and training
approaches, including same-project training, cross-project training, training a
model especially designed to be sample efficient (and thus prima facie
well-suited for learning in a limited-sample same-project setting) and a
maximalist hybrid approach, fine-tuning first on many projects in many
languages and then training on the same-project. We find that the maximalist
hybrid setting provides consistent, substantial gains over the
state-of-the-art, on many different projects in both Java and Python.
