标题： 基于自然语言查询的结构化代码搜索 显示英文标题

标题： Structural Code Search using Natural Language Queries

摘要： 搜索代码是开发人员执行的常见任务，以理解API、学习常见的代码模式和导航代码。目前，开发人员最常用的关键字和正则表达式进行搜索，这些方法易于使用且广泛可用。除了关键字和正则表达式之外，结构化代码搜索工具允许开发人员根据语法结构搜索代码。这在从错误查找系统地重构代码等各种应用中都有用处。然而，这些结构化代码搜索工具的操作是通过领域特定语言（DSL）编写的查询，这些语言可能难以学习和编写。我们提出允许开发人员使用自然语言进行结构化代码搜索。用自然语言表达查询提供了一种直观的代码搜索方式，并降低了入门门槛。在这项工作中，我们开发了一种新颖的通用方法，将大型语言模型（LLM）的推理能力结合起来，以解释自然语言搜索查询，并利用结构化搜索工具的强大功能来高效准确地检索相关代码。然后，我们将这种方法应用于两种结构化代码搜索DSL：Semgrep和GQL。在我们的评估中，我们构建了一个新的结构化代码搜索基准，包含10个Java项目上的400个查询。我们表明，基于使用LLM将自然语言查询转换为DSL查询的结构化代码搜索方法是有效且稳健的，在精度和召回率方面达到55% - 70%的高水平。此外，我们的方法在F1分数上比基于语义代码搜索和LLM检索的基线方法显著优越，最高可达57%和14%。 显示英文摘要

摘要： Searching code is a common task that developers perform to understand APIs, learn common code patterns, and navigate code. Currently, developers most commonly search using keywords and regular expressions that are easy to use and widely available. Beyond keywords and regular expressions, structural code search tools allow developers to search for code based on its syntactic structure. This has numerous applications ranging from bug finding to systematically refactoring code. However, these structural code search tools operate on queries expressed in domain-specific languages (DSL) that can be difficult to learn and write. We propose to allow developers to use natural language to search for code structurally. Expressing queries in natural language provides an intuitive way to search for code and lowers the barrier to entry. In this work, we develop a novel general approach that combines the reasoning capabilities of an LLM to interpret natural language search queries with the power of structural search tools to efficiently and accurately retrieve relevant code. We then instantiate this approach for two structural code search DSLs: Semgrep and GQL. In our evaluation, we construct a new benchmark for structural code search consisting of 400 queries over 10 Java projects. We show that our approach for structural code search based on translating NL queries to DSL queries using an LLM is effective and robust, achieving a high precision and recall ranging from 55% - 70%. Further, our approach significantly outperforms baselines based on semantic code search and LLM retrievals by up to 57% and 14% on F1 scores.