RACK：使用众包知识在IDE中进行代码搜索
RACK: Code Search in the IDE using Crowdsourced Knowledge
摘要：
传统的代码搜索引擎通常不能很好地执行自然语言查询，因为它们主要应用关键字匹配。因此，这些引擎需要精心设计的查询，其中包含有关代码搜索编程API的信息。不幸的是，现有的研究表明，准备一个有效的查询代码搜索是既具有挑战性和耗时的开发人员。在本文中，我们提出了一种新的代码搜索工具-RACK-返回相关的源代码为一个给定的代码搜索查询写在自然语言文本。该工具首先通过从Stack Overflow的众包知识中挖掘关键字-API关联来将查询转换为相关API类的列表，然后将重新定义的查询应用于GitHub代码搜索API以收集相关结果。一旦提交了与编程任务相关的查询，该工具就会自动从数千个开源项目中挖掘相关的代码片段，并在开发人员的编程环境（IDE）的上下文中将其显示为排名列表。工具页：http://www.usask.ca/~masud.rahman/rack
英文摘要：
Traditional code search engines often do not perform well with natural language queries since they mostly apply keyword matching. These engines thus require carefully designed queries containing information about programming APIs for code search. Unfortunately, existing studies suggest that preparing an effective query for code search is both challenging and time consuming for the developers. In this paper, we propose a novel code search tool--RACK--that returns relevant source code for a given code search query written in natural language text. The tool first translates the query into a list of relevant API classes by mining keyword-API associations from the crowdsourced knowledge of Stack Overflow, and then applies the reformulated query to GitHub code search API for collecting relevant results. Once a query related to a programming task is submitted, the tool automatically mines relevant code snippets from thousands of open-source projects, and displays them as a ranked list within the context of the developer's programming environment--the IDE. Tool page: http://www.usask.ca/~masud.rahman/rack
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
