Large language models of code (Code-LLMs) have recently brought tremendous
advances to code completion, a fundamental feature of programming assistance
and code intelligence. However, most existing works ignore the possible
presence of bugs in the code context for generation, which are inevitable in
software development. Therefore, we introduce and study the buggy-code
completion problem, inspired by the realistic scenario of real-time code
suggestion where the code context contains potential bugs -- anti-patterns that
can become bugs in the completed program. To systematically study the task, we
introduce two datasets: one with synthetic bugs derived from semantics-altering
operator changes (buggy-HumanEval) and one with realistic bugs derived from
user submissions to coding problems (buggy-FixEval). We find that the presence
of potential bugs significantly degrades the generation performance of the
high-performing Code-LLMs. For instance, the passing rates of CodeGen-2B-mono
on test cases of buggy-HumanEval drop more than 50% given a single potential
bug in the context. Finally, we investigate several post-hoc methods for
mitigating the adverse effect of potential bugs and find that there remains a
large gap in post-mitigation performance.

介绍和研究有潜在 bug 的代码补全问题，引入两个数据集，发现潜在 bug 显著降低 Code-LLMs 的性能，调查了几种后处理方法，但效果有限。