Addressing scalability in API method call analytics
Pages 1 - 7
Abstract
Intelligent code completion recommends relevant code to developers by comparing the editor content to code patterns extracted by analyzing large repositories. However, with the vast amount of data available in such repositories, scalability of the recommender system becomes an issue. We propose using Boolean Matrix Factorization (BMF) as a clustering technique for analyzing code in order to improve scalability of the underlying models. We compare model size, inference speed, and prediction quality of an intelligent method call completion engine built on top of canopy clustering versus one built on top of BMF. Our results show that BMF reduces model size up to 80% and increases inference speed up to 78%, without significant change in prediction quality.
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- Addressing scalability in API method call analytics
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Published In
November 2016
53 pages
ISBN:9781450343954
DOI:10.1145/2989238
- General Chairs:
- Olga Baysal,
- Jacek Czerwonka,
- Latifa Guerrouj,
- David Lo,
- Brendan Murphy
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Published: 13 November 2016
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FSE'16: 24nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering
November 13, 2016
WA, Seattle, USA
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