Awesome

Deep Code Search

Code for the ICSE 2018 paper Deep Code Search.

Two Versions

We release both Keras and PyTorch code of our approach, in the keras and pytorch folders, respectively.

• The Keras folder contains the code to run the experiments presented in the paper. The code is frozen to what it was when we originally wrote the paper. (NOTE: we modified some deprecated API invocations to fit for the latest Keras and theano).

• The PyTorch is the bleeding-edge reporitory where we packaged it up, improved the code quality and added some features.

⚠️ Note that the PyTorch version is problematic at present. For those who want to replicate DeepCS as a baseline model, it is highly recommended to check out the Keras version. This could greatly save your time and effort.

🤗 Nevertheless, if you are interested in using and improving DeepCS, check out the PyTorch version and feel free to contribute.

For more information, please refer to the README files under the directory of each component.

Tool Demo

An online tool demo can be found in http://211.249.63.55:81/ (Unavailable due to budget constraint)

Citation

If you find it useful and would like to cite it, the following would be appropriate:

@inproceedings{gu2018deepcs,
  title={Deep Code Search},
  author={Gu, Xiaodong and Zhang, Hongyu and Kim, Sunghun},
  booktitle={Proceedings of the 2018 40th International Conference on Software Engineering (ICSE 2018)},
  year={2018},
  organization={ACM}
}