  Code linters play a crucial role in developing high-quality software systems
by detecting potential problems (e.g., memory leaks) in the source code of
systems. Despite their benefits, code linters are often language-specific,
focused on certain types of issues, and prone to false positives in the
interest of speed. This paper investigates whether large language models can be
used to develop a more versatile code linter. Such a linter is expected to be
language-independent, cover a variety of issue types, and maintain high speed.
To achieve this, we collected a large dataset of code snippets and their
associated issues. We then selected a language model and trained two
classifiers based on the collected datasets. The first is a binary classifier
that detects if the code has issues, and the second is a multi-label classifier
that identifies the types of issues. Through extensive experimental studies, we
demonstrated that the developed large language model-based linter can achieve
an accuracy of 84.9% for the binary classifier and 83.6% for the multi-label
classifier.
