Automated library recommendation

Ferdian Thung 1 David Lo 1 Julia Lawall 2
2 Regal - Large-Scale Distributed Systems and Applications
LIP6 - Laboratoire d'Informatique de Paris 6, Inria Paris-Rocquencourt
Abstract : Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association rule mining and collaborative filtering. The association rule mining component recommends libraries based on a set of library usage patterns. The collaborative filtering component recommends libraries based on those that are used by other similar projects. We investigate the effectiveness of our hybrid approach on 500 software projects that use many third-party libraries. Our experiments show that our approach can recommend libraries with recall rate@5 of 0.852 and recall rate@10 of 0.89
Type de document :
Communication dans un congrès
Ralf Lämmel and Rocco Oliveto and Romain Robbes. WCRE 2013 - 20th Working Conference on Reverse Engineering, Oct 2013, Koblenz, Germany. IEEE, pp.182-191, 2013, 〈10.1109/WCRE.2013.6671293〉
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https://hal.inria.fr/hal-00918076
Contributeur : Julia Lawall <>
Soumis le : jeudi 12 décembre 2013 - 20:51:51
Dernière modification le : vendredi 25 mai 2018 - 12:02:03

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Ferdian Thung, David Lo, Julia Lawall. Automated library recommendation. Ralf Lämmel and Rocco Oliveto and Romain Robbes. WCRE 2013 - 20th Working Conference on Reverse Engineering, Oct 2013, Koblenz, Germany. IEEE, pp.182-191, 2013, 〈10.1109/WCRE.2013.6671293〉. 〈hal-00918076〉

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