Recent years have witnessed the deployment of code language models (LMs) in
various code intelligence tasks such as code completion. Yet, it is challenging
for pre-trained LMs to generate correct completions in private repositories.
Previous studies retrieve cross-file context based on import relations or text
similarity, which is insufficiently relevant to completion targets. In this
paper, we propose a dataflow-guided retrieval augmentation approach, called
DraCo, for repository-level code completion. DraCo parses a private repository
into code entities and establishes their relations through an extended dataflow
analysis, forming a repo-specific context graph. Whenever triggering code
completion, DraCo precisely retrieves relevant background knowledge from the
repo-specific context graph and generates well-formed prompts to query code
LMs. Furthermore, we construct a large Python dataset, ReccEval, with more
diverse completion targets. Our experiments demonstrate the superior accuracy
and applicable efficiency of DraCo, improving code exact match by 3.43% and
identifier F1-score by 3.27% on average compared to the state-of-the-art
approach.

近年来，代码语言模型在代码智能任务中的部署日益增多，但是预训练模型要在私有仓库中生成正确的自动补全是具有挑战性的。本文提出了一种名为 DraCo 的数据流引导检索增强方法，用于仓库级别的代码自动补全，其通过扩展的数据流分析将私有仓库解析为代码实体并建立它们之间的关系，形成一个仓库特定的上下文图。每当触发代码自动补全时，DraCo 会精确地从仓库特定的上下文图中检索相关的背景知识，并生成良好的代码查询提示。此外，我们构建了一个包含更多不同补全目标的大型 Python 数据集 ReccEval。实验证明了 DraCo 的卓越准确性和适用性，与最先进的方法相比，在精确匹配代码方面平均提升了 3.43%，在标识符 F1 - 分数方面平均提升了 3.27%。

数据流导向的代码库级代码补全增强

Dataflow-Guided Retrieval Augmentation for Repository-Level Code  Completion

Distributed model training is vulnerable to byzantine system failures and
adversarial compute nodes, i.e., nodes that use malicious updates to corrupt
the global model stored at a parameter server (PS). To guarantee some form of
robustness, recent work suggests using variants of the geometric median as an
aggregation rule, in place of gradient averaging. Unfortunately, median-based
rules can incur a prohibitive computational overhead in large-scale settings,
and their convergence guarantees often require strong assumptions. In this
work, we present DRACO, a scalable framework for robust distributed training
that uses ideas from coding theory. In DRACO, each compute node evaluates
redundant gradients that are used by the parameter server to eliminate the
effects of adversarial updates. DRACO comes with problem-independent robustness
guarantees, and the model that it trains is identical to the one trained in the
adversary-free setup. We provide extensive experiments on real datasets and
distributed setups across a variety of large-scale models, where we show that
DRACO is several times, to orders of magnitude faster than median-based
approaches.

通过编码理论，我们提出了 DRACO，一个可伸缩的分布式训练框架，用于解决分布式模型训练中的拜占庭系统故障和对抗计算节点等问题，DRACO 具有问题无关的鲁棒性保证，并且训练的模型与无对手的设置中训练的模型相同。