Design and evaluation of a multi-recommendation system for local code search. (April 2017)

• Record Type: Journal Article
• Authors: Ge, Xi; Shepherd, David C.; Damevski, Kostadin; Murphy-Hill, Emerson
• Is Part Of: Journal of visual languages & computing. Volume 39(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-04
• Subjects: Code search -- Recommender systems -- Field study; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2016.07.002
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: 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.