@inproceedings{56a12a4051134cecbee3d70e367562ad,
title = "Addressing scalability in API method call analytics",
abstract = "Intelligent code completion recommends relevant code to de- velopers 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 signifficant change in prediction quality.",
keywords = "Analytics of code repositories, Boolean Matrix Factorization, Intelligent method call completion, Scalability",
author = "Ervina Cergani and Sebastian Proksch and Sarah Nadi and Mira Mezini",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 2nd International Workshop on Software Analytics, SWAN 2016 ; Conference date: 13-11-2016",
year = "2016",
month = nov,
day = "13",
doi = "10.1145/2989238.2989240",
language = "English (US)",
series = "SWAN 2016 - Proceedings of the 2nd International Workshop on Software Analytics, co-located with FSE 2016",
publisher = "Association for Computing Machinery, Inc",
pages = "1--7",
editor = "Latifa Guerrouj and David Lo and Olga Baysal and Jacek Czerwonka and Brendan Murphy",
booktitle = "SWAN 2016 - Proceedings of the 2nd International Workshop on Software Analytics, co-located with FSE 2016",
}