  A code summary is a brief natural language description of source code.
Summaries are usually only a single sentence long, and yet form the backbone of
developer documentation. A short descriptions such as "changes all visible
polygons to the color blue" can give a programmer a high-level idea of what
code does without the effort of reading the code itself. Recently, products
based on Large Language Models such as ChatGPT have demonstrated a strong
ability to write these descriptions automatically. However, to use these tools,
programmers must send their code to untrusted third parties for processing
(e.g., via an API call). This loss of custody is not acceptable to many
organizations. In this paper, we present an alternative: we train an open
source model using sample output generated by GPT-3.5 in a process related to
knowledge distillation. Our model is small enough (350m parameters) to be run
on a single 16gb GPU, yet we show in our evaluation that it is large enough to
mimic GPT-3.5 on this task.

Software Engineering;Artificial Intelligence