In this work, we propose and study annotated code search: the retrieval of
code snippets paired with brief descriptions of their intent using natural
language queries. On three benchmark datasets, we investigate how code
retrieval systems can be improved by leveraging descriptions to better capture
the intents of code snippets. Building on recent progress in transfer learning
and natural language processing, we create a domain-specific retrieval model
for code annotated with a natural language description. We find that our model
yields significantly more relevant search results (with absolute gains up to
20.6% in mean reciprocal rank) compared to state-of-the-art code retrieval
methods that do not use descriptions but attempt to compute the intent of
snippets solely from unannotated code.

提出并研究了注释代码搜索：使用自然语言查询检索与简短描述其意图的代码片段。通过在三个基准数据集上研究如何利用描述来更好地捕捉代码片段的意图，建立了一种用自然语言描述注释的领域特定检索模型。发现相对于不使用描述但仅尝试从未注释的代码中计算片段意图的最新代码检索方法，我们的模型可以获得显著更相关的搜索结果（平均倒数排名的绝对增益高达 20.6％）。