  We address the problem of predicting edit completions based on a learned
model that was trained on past edits. Given a code snippet that is partially
edited, our goal is to predict a completion of the edit for the rest of the
snippet. We refer to this task as the EditCompletion task and present a novel
approach for tackling it. The main idea is to directly represent structural
edits. This allows us to model the likelihood of the edit itself, rather than
learning the likelihood of the edited code. We represent an edit operation as a
path in the program's Abstract Syntax Tree (AST), originating from the source
of the edit to the target of the edit. Using this representation, we present a
powerful and lightweight neural model for the EditCompletion task.
  We conduct a thorough evaluation, comparing our approach to a variety of
representation and modeling approaches that are driven by multiple strong
models such as LSTMs, Transformers, and neural CRFs. Our experiments show that
our model achieves a 28% relative gain over state-of-the-art sequential models
and 2x higher accuracy than syntactic models that learn to generate the edited
code, as opposed to modeling the edits directly.
  Our code, dataset, and trained models are publicly available at
https://github.com/tech-srl/c3po/ .
