Design and evaluation of a multi-recommendation system for local code search

Searching for relevant code in the local code base is a common activity during software maintenance. However, previous research indicates that 88% of manually-composed search queries retrieve no relevant results. One reason that many searches fail is existing search tools’ dependence on string matching algorithms, which cannot find semantically-related code. To solve this problem by helping developers compose better queries, researchers have proposed numerous query recommendation techniques, relying on a variety of dictionaries and algorithms. However, few of these techniques are empirically evaluated by usage data from real-world developers. To fill this gap, we designed a multi-recommendation system that relies on the cooperation between several query recommendation techniques. We implemented and deployed this recommendation system within the Sando code search tool and conducted a longitudinal field study. Our study shows that over 34% of all queries were adopted from recommendation; and recommended queries retrieved results 11% more often than manual queries.