RACK: Code Search in the IDE using Crowdsourced Knowledge
This addresses the challenge for developers in preparing effective queries for code search, though it is incremental as it builds on existing crowdsourced knowledge and APIs.
The authors tackled the problem of ineffective natural language queries in code search by developing RACK, a tool that translates queries into relevant API classes using Stack Overflow data and retrieves ranked code snippets from GitHub, achieving automated code search within the IDE.
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