Summarizing Source Code using a Neural Attention Model

@inproceedings{Iyer2016SummarizingSC,
  title={Summarizing Source Code using a Neural Attention Model},
  author={Srinivasan Iyer and Ioannis Konstas and Alvin Cheung and Luke S. Zettlemoyer},
  booktitle={ACL},
  year={2016}
}
High quality source code is often paired with high level summaries of the computation it performs, for example in code documentation or in descriptions posted in online forums. Such summaries are extremely useful for applications such as code search but are expensive to manually author, hence only done for a small fraction of all code that is produced. In this paper, we present the first completely datadriven approach for generating high level summaries of source code. Our model, CODE-NN , uses… CONTINUE READING
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