  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
