  During maintenance, software developers deal with numerous change requests
made by the users of a software system. Studies show that the developers find
it challenging to select appropriate search terms from a change request during
concept location. In this paper, we propose a novel technique--QUICKAR--that
automatically suggests helpful reformulations for a given query by leveraging
the crowdsourced knowledge from Stack Overflow. It determines semantic
similarity or relevance between any two terms by analyzing their adjacent word
lists from the programming questions of Stack Overflow, and then suggests
semantically relevant queries for concept location. Experiments using 510
queries from two software systems suggest that our technique can improve or
preserve the quality of 76% of the initial queries on average which is
promising. Comparison with one baseline technique validates our preliminary
findings, and also demonstrates the potential of our technique.
