Title

RACK: Code search in the IDE using crowdsourced knowledge

Publication Type

Conference Proceeding Article

Publication Date

5-2017

Abstract

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.

Keywords

Tools, Natural languages, Programming, Search engines, Context, Search problems, Vocabulary

Discipline

Software Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of the 39th ACM/IEEE International Conference on Software Engineering (ICSE 2017)

ISBN

978-1-5386-1589-8

Identifier

10.1109/ICSE-C.2017.11

Publisher

IEEE

City or Country

Buenos Aires, Argentina

Additional URL

http://doi.org/10.1109/ICSE-C.2017.11

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