  The advent of Large Language Models (LLMs) has revolutionized various domains
of artificial intelligence, including the realm of software engineering. In
this research, we evaluate the efficacy of pre-trained LLMs in replicating the
tasks traditionally performed by developers in response to code review
comments. We provide code contexts to five popular LLMs and obtain the
suggested code-changes (patch sets) derived from real-world code-review
comments. The performance of each model is meticulously assessed by comparing
their generated patch sets against the historical data of human-generated
patch-sets from the same repositories. This comparative analysis aims to
determine the accuracy, relevance, and depth of the LLMs' feedback, thereby
evaluating their readiness to support developers in responding to code-review
comments.
  Novelty: This particular research area is still immature requiring a
substantial amount of studies yet to be done. No prior research has compared
the performance of existing Large Language Models (LLMs) in code-review
comments. This in-progress study assesses current LLMs in code review and paves
the way for future advancements in automated code quality assurance, reducing
context-switching overhead due to interruptions from code change requests.
