Conference Proceeding Article
Traditional code search engines often do not perform well with natural language queries since they mostly apply keyword matching. These engines thus need carefully designed queries containing information about programming APIs for code search. Unfortunately, existing studies suggest that preparing an effective code search query is both challenging and time consuming for the developers. In this paper, we propose a novel API recommendation technique -- RACK that recommends a list of relevant APIs for a natural language query for code search by exploiting keyword-API associations from the crowdsourced knowledge of Stack Overflow. We first motivate our technique using an exploratory study with 11 core Java packages and 344K Java posts from Stack Overflow. Experiments using 150 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the top 10 results for about 79% of the queries which is highly promising. Comparison with two variants of the state-of-the-art technique also shows that RACK outperforms both of them not only in Top-K accuracy but also in mean average precision and mean recall by a large margin.
Stack Overflow, Code search, query reformulation, keyword-API association, crowdsourced knowledge
Software and Cyber-Physical Systems
2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER): March 14-18, Osaka: Proceedings
City or Country
RAHMAN, Mohammad M.; ROY, Chanchal K.; and LO, David.
RACK: Automatic API recommendation using crowdsourced knowledge. (2016). 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER): March 14-18, Osaka: Proceedings. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3725
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