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RACK: Code Search in the IDE using Crowdsourced Knowledge

Author email: masud.rahman@usask.ca
Tool name: RACK
Description: 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
Bibtex: "@inproceedings{rahman2017rack, title={RACK: Code search in the IDE using crowdsourced knowledge}, author={Rahman, Mohammad Masudur and Roy, Chanchal K and Lo, David}, booktitle={2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C)}, pages={51--54}, year={2017}, organization={IEEE} }"
Link to public pdf: https://dl.acm.org/citation.cfm?id=3098361
Link to tool webpage: http://homepage.usask.ca/~masud.rahman/rack/
Link to demo: Not provided by authors
Category: None
Tags: stack overflow, crowdsourced knowledge, query reformulation, code search, keyword-api association
Artifacts:
Year and Conference: 2017, ICSE
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