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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Santos, A. L., Prendi, G., Sousa, H. & Ribeiro, R. (2017). Stepwise API usage assistance using n-gram language models. Journal of Systems and Software. 131, 461-474
Exportar Referência (IEEE) 
A. L. Santos et al.,  "Stepwise API usage assistance using n-gram language models", in Journal of Systems and Software, vol. 131, pp. 461-474, 2017
Exportar BibTeX 
@article{santos2017_1714049274309,
	author = "Santos, A. L. and Prendi, G. and Sousa, H. and Ribeiro, R.",
	title = "Stepwise API usage assistance using n-gram language models",
	journal = "Journal of Systems and Software",
	year = "2017",
	volume = "131",
	number = "",
	doi = "10.1016/j.jss.2016.06.063",
	pages = "461-474",
	url = "http://www.sciencedirect.com/science/article/pii/S0164121216300917"
}
Exportar RIS 
TY  - JOUR
TI  - Stepwise API usage assistance using n-gram language models
T2  - Journal of Systems and Software
VL  - 131
AU  - Santos, A. L.
AU  - Prendi, G.
AU  - Sousa, H.
AU  - Ribeiro, R.
PY  - 2017
SP  - 461-474
SN  - 0164-1212
DO  - 10.1016/j.jss.2016.06.063
UR  - http://www.sciencedirect.com/science/article/pii/S0164121216300917
AB  - Reusing software involves learning third-party APIs, a process that is often time-consuming and error-prone. Recommendation systems for API usage assistance based on statistical models built from source code corpora are capable of assisting API users through code completion mechanisms in IDEs. A valid sequence of API calls involving different types may be regarded as a well-formed sentence of tokens from the API vocabulary. In this article we describe an approach for recommending subsequent tokens to complete API sentences using n-gram language models built from source code corpora. The provided system was integrated in the code completion facilities of the Eclipse IDE, providing contextualized completion proposals for Java taking into account the nearest lines of code. The approach was evaluated against existing client code of four widely used APIs, revealing that in more than 90% of the cases the expected subsequent token is within the 10-top-most proposals of our models. The high score provides evidence that the recommendations could help on API learning and exploration, namely through the assistance on writing valid API sentences.
ER  -