Automated library recommendation
作者： Ferdian Thung , David Lo , Julia Lawall
DOI: 10.1109/WCRE.2013.6671293
关键词:
摘要: Many third party libraries are available to be downloaded and used. Using such can reduce development time make the developed software more reliable. However, developers often unaware of suitable used for their projects thus they miss out on these benefits. To help better take advantage libraries, we propose a new technique that automatically recommends developers. Our takes as input set an application currently uses, other likely relevant. We follow hybrid approach combines association rule mining collaborative filtering. The component based library usage patterns. filtering those by similar projects. investigate effectiveness our 500 use many third-party libraries. experiments show recommend with recall rate@5 0.852 rate@10 0.894.
smu.edu.sg
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