FSE 2016 All Events

24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2016), November 13–18, 2016, Seattle, WA, USA

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Session 11: Recommendation
Research Papers
Emerald 2, Chair: Chris Bird
API Code Recommendation using Statistical Learning from Fine-Grained Changes
Anh Tuan Nguyen, Michael Hilton, Mihai Codoban, Hoan Anh Nguyen, Lily Mast, Eli Rademacher, Tien N. Nguyen, and Danny Dig
(Iowa State University, USA; Oregon State University, USA; Microsoft, USA; University of Evansville, USA)
Publisher's Version
Abstract: Learning and remembering how to use APIs is difficult. While code-completion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious. Moreover, users can easily be overwhelmed with too much information. We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers. Our approach and tool, APIREC, is based on statistical learning from fine-grained code changes and from the context in which those changes were made. Our empirical evaluation shows that APIREC correctly recommends an API call in the first position 59% of the time, and it recommends the correct API call in the top five positions 77% of the time. This is a significant improvement over the state-of-the-art approaches by 30-160% for top-1 accuracy, and 10-30% for top-5 accuracy, respectively. Our result shows that APIREC performs well even with a one-time, minimal training dataset of 50 publicly available projects.

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Time stamp: 2019-05-25T01:50:32+02:00