@InProceedings{loyola-marresetaylor-matsuo:2017:Short,
  author    = {Loyola, Pablo  and  Marrese-Taylor, Edison  and  Matsuo, Yutaka},
  title     = {A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {287--292},
  abstract  = {We propose a model to automatically describe changes introduced in the source
	code of a program using natural language. Our method receives as input a set of
	code commits, which contains both the modifications and  message introduced by
	an user. These two modalities are used to train  an encoder-decoder
	architecture. We evaluated our approach on twelve real world open source
	projects from four different programming languages. Quantitative and
	qualitative results showed that the proposed approach can generate feasible and
	semantically sound descriptions not only in standard in-project settings, but
	also in a cross-project setting.},
  url       = {http://aclweb.org/anthology/P17-2045}
}