Design and evaluation of a multi-recommendation system for local code search |
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| Affiliation: | 1. Apple Inc., Cupertino, CA, USA;2. ABB Corporate Research, Raleigh, NC, USA;3. Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA;4. Department of Computer Science, North Carolina State University, Raleigh, NC, USA;1. Centre for Computing and Engineering Software and Systems (SUCCESS), Swinburne University of Technology, Hawthorn 3122, VIC, Australia;2. Institute of Software Technology, Universität Stuttgart, Universitätsstraß e 38, D-70569 Stuttgart, Germany;1. CACS, UL Lafayette, United States;2. School of EECS, Oregon State University, United States;1. University of Illinois at Urbana-Champaign, Urbana, IL, USA;2. National Laboratory of Radar Signal Processing, Xidian University, Xi׳an, Shaanxi, China;1. University of Mannheim, B6, C2.11, Mannheim, Germany;2. Victoria University of Wellington, P.O. Box 600, Wellington 6140, New Zealand |
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| 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. |
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| Keywords: | Code search Recommender systems Field study |
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