How developers use multi-recommendation system in local code search

Developers often start programming tasks by searching for relevant code in their local codebase. Previous research suggests that 88% of manually-composed queries retrieve no relevant results. Many searches fail because existing search tools depend solely on string matching with a manually-composed query, which cannot find semantically-related code. To solve this problem, researchers proposed query recommendation techniques to help developers compose queries without the extensive knowledge of the codebase under search. However, few of these techniques are empirically evaluated by the usage data from real-world developers. To fill this gap, we studied several query recommendation techniques by extending Sando and conducting a longitudinal field study. Our study shows that over 30% of all queries were adopted from recommendation; and recommended queries retrieved results 7% more often than manual queries.