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Software developers frequently issue generic natural language queries forcode search while using code search engines (e.g., GitHub native search,Krugle). Such queries often do not lead to any relevant results due tovocabulary mismatch problems. In this paper, we propose a novel technique thatautomatically identifies relevant and specific API classes from Stack OverflowQ & A site for a programming task written as a natural language query, and thenreformulates the query for improved code search. We first collect candidate APIclasses from Stack Overflow using pseudo-relevance feedback and two termweighting algorithms, and then rank the candidates using Borda count andsemantic proximity between query keywords and the API classes. The semanticproximity has been determined by an analysis of 1.3 million questions andanswers of Stack Overflow. Experiments using 310 code search queries reportthat our technique suggests relevant API classes with 48% precision and 58%recall which are 32% and 48% higher respectively than those of thestate-of-the-art. Comparisons with two state-of-the-art studies and threepopular search engines (e.g., Google, Stack Overflow, and GitHub native search)report that our reformulated queries (1) outperform the queries of thestate-of-the-art, and (2) significantly improve the code search resultsprovided by these contemporary search engines.

Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics