As software projects progress, quality of code assumes paramount importance
as it affects reliability, maintainability and security of software. For this
reason, static analysis tools are used in developer workflows to flag code
quality issues. However, developers need to spend extra efforts to revise their
code to improve code quality based on the tool findings. In this work, we
investigate the use of (instruction-following) large language models (LLMs) to
assist developers in revising code to resolve code quality issues. We present a
tool, CORE (short for COde REvisions), architected using a pair of LLMs
organized as a duo comprised of a proposer and a ranker. Providers of static
analysis tools recommend ways to mitigate the tool warnings and developers
follow them to revise their code. The \emph{proposer LLM} of CORE takes the
same set of recommendations and applies them to generate candidate code
revisions. The candidates which pass the static quality checks are retained.
However, the LLM may introduce subtle, unintended functionality changes which
may go un-detected by the static analysis. The \emph{ranker LLM} evaluates the
changes made by the proposer using a rubric that closely follows the acceptance
criteria that a developer would enforce. CORE uses the scores assigned by the
ranker LLM to rank the candidate revisions before presenting them to the
developer. CORE could revise 59.2% Python files (across 52 quality checks) so
that they pass scrutiny by both a tool and a human reviewer. The ranker LLM is
able to reduce false positives by 25.8% in these cases. CORE produced revisions
that passed the static analysis tool in 76.8% Java files (across 10 quality
checks) comparable to 78.3% of a specialized program repair tool, with
significantly much less engineering efforts.

利用大型语言模型辅助开发人员修订代码以解决代码质量问题，通过使用建议生成候选代码修订方案并使用排名模型对其进行评估，该方法能够有效减少错误警告并提高通过静态分析工具的代码比例。

代码质量问题困扰？LLM 可以帮助！

Frustrated with Code Quality Issues? LLMs can Help!

WikiHow is an open-domain repository of instructional articles for a variety
of tasks, which can be revised by users. In this paper, we extract pairwise
versions of an instruction before and after a revision was made. Starting from
a noisy dataset of revision histories, we specifically extract and analyze
edits that involve cases of vagueness in instructions. We further investigate
the ability of a neural model to distinguish between two versions of an
instruction in our data by adopting a pairwise ranking task from previous work
and showing improvements over existing baselines.

通过对 WikiHow 教学文章的修订版本进行分析，本文研究了指导指令中的模糊性，并采用神经模型的配对排名任务，展示了对指令版本的改进。