FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns
Autor: | Phuong T. Nguyen, Thomas Degueule, Davide Di Ruscio, Massimiliano Di Penta, Juri Di Rocco, Lina Ochoa |
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Přispěvatelé: | Università degli Studi dell'Aquila (UNIVAQ), Centrum Wiskunde & Informatica (CWI), University of Sannio [Benevento] |
Rok vydání: | 2019 |
Předmět: |
recommender system
api mining Java Computer science business.industry api recommendation api usage pattern 020207 software engineering [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] 02 engineering and technology Recommender system World Wide Web Software 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business computer computer.programming_language |
Zdroj: | ICSE 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) 41st ACM/IEEE International Conference on Software Engineering (ICSE) 41st ACM/IEEE International Conference on Software Engineering (ICSE), May 2019, Montréal, Canada |
DOI: | 10.1109/icse.2019.00109 |
Popis: | International audience; Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative-filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns. |
Databáze: | OpenAIRE |
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