  Recent years have seen the development of LLM-based code generation. Compared
to generating code in a software project, incremental code edits are
empirically observed to be more frequent. The emerging code editing approaches
usually formulate the problem as generating an edit based on known relevant
prior edits and context. However, practical code edits can be more complicated.
First, an editing session can include multiple (ir)relevant edits to the code
under edit. Second, the inference of the subsequent edits is non-trivial as the
scope of its ripple effect can be the whole project. In this work, we propose
CoEdPilot, an LLM-driven solution to recommend code edits by discriminating the
relevant edits, exploring their interactive natures, and estimating its ripple
effect in the project. Specifically, CoEdPilot orchestrates multiple neural
transformers to identify what and how to edit in the project regarding both
edit location and edit content. When a user accomplishes an edit with an
optional editing description, a Subsequent Edit Analysis first reports the most
relevant files in the project with what types of edits (e.g., keep, insert, and
replace) can happen for each line of their code. Next, an Edit-content
Generator generates concrete edit options for the lines of code, regarding its
relevant prior changes reported by an Edit-dependency Analyzer. Lastly, both
the Subsequent Edit Analysis and the Edit-content Generator capture relevant
prior edits as feedback to readjust their recommendations. We train our models
by collecting over 180K commits from 471 open-source projects in 5 programming
languages. Our extensive experiments show that CoEdPilot can well predict the
edits (i.e., predicting edit location with an accuracy of 70.8%-85.3%, and the
edit content with an exact match rate of 41.8% and BLEU4 score of 60.7)...
