AutoenCODE

AutoenCODE is a Deep Learning infrastructure that allows to encode source code fragments into vector representations, which can be used to learn similarities.

This repository contains code, data, and instructions on how to learn sentence-level embeddings for a given textual corpus (source code, or any other textual corpus). The learned embeddings (i.e., continous-valued vectors) can then be used to identify similarities among the sentences in the corpus. AutoenCODE uses a Neural Network Language Model (word2vec[3]), which pre-trains word embeddings in the corpus, and a Recursive Neural Network (Recursive Autoencoder[4]) that recursively combines embeddings to learn sentence-level embeddings.

AutoenCODE was built by Martin White and Michele Tufano and used and adapted in the context of the following research projects. If you are using AutoenCODE for research purposes, please cite:

• [1] Deep Learning Code Fragments for Code Clone Detection [paper, website]
• [2] Deep Learning Similarities from Different Representations of Source Code [paper, website]

The repository contains the original source code for word2vec[3] and a forked/modified implementation of a Recursive Autoencoder[4]. Please refer to the bibliography section to appropriately cite the following papers:

• [3] Efficient Estimation of Word Representations in Vector Space
• [4] Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions

Corpus

With the term corpus we refer to a collection of sentences for which we aim to learn vector representations (embeddings). Each sentence can be anything in textual format: a natural language phrase or chapter, a piece of source code (expressed as plain code or stream of lexical/AST terms), etc.

A single text file contains the entire corpus where each line represents a sentence in the corpus. An example can be found in data/corpus.src.

Learn Word embeddings

In this stage we use word2vec to train a language model in order to learn word embeddings for each term in the corpus. These vectors will be used as pre-trained embeddings for the recursive autoencoder. Other language models can be used to learn word embeddings, such as an RNN LM (RNNLM Toolkit).

Build word2vec

The folder bin/word2vec contains the source code for word2vec. You can build the program with:

cd bin/word2vec
make

run_word2vec.sh

run_word2vec.sh computes word embeddings for any text corpus. The inputs are:

• the path of the directory containing the text corpus corpus.src;
• the path of the output directory;
• the size of the word vectors.

The output of word2vec is written into the word2vec.out file. The number of lines in the output is equal to the vocabulary size plus one. The first line is a header that contains the vocabulary size and the number of hidden units. Each subsequent line contains a lexical element first and then its embedding splayed on the line. For example, if the size of the word vectors is equal to 400, then the lexical element public will begin a line in word2vec.out followed by 400 doubles each separated by one space. This output serves as a dictionary that maps lexical elements to continuous-valued vectors. These vectors can be visualized using a dimensionality reduction technique such as t-SNE.

run_postprocess.py

bin/run_postprocess.py is a utility for parsing word2vec output. Run the script as follow:

./run_postprocess.py --w2v <path/to/word2vec.out>  --src <path/to/corpus/folder/>

Where <path/to/word2vec.out> is the path to the word2vec.out file, and <path/to/corpus/folder/> is the path to the directory containing the corpus.src file. The utility parses word2vec.out into a vocab.txt (containing the list of terms) and an embed.txt (containing the matrix of embeddings). Then the utility uses the index of each term in the list of terms to transform the src2txt .src files into .int files where the lexical elements are replaced with integers. In other words, suppose the lexical element public is listed on line #5 of vocab.txt. The embedding for public will be on line #5 of embed.txt and every instance of public in corpus.src will be replaced with the number 5 in corpus.int.

Learn Sentence embeddings

In this stage we use a recursive autoencoder which recursively combines embeddings - starting from the word embeddings generated in the previous stage - to learn sentence-level embeddings. Then, distances among the embeddings are computed and saved in a distance matrix which can be analyzed in order to discover similarities among the sentences in the corpus.

Recursive Autoencoder

rae/run_rae.sh runs the recursive autoencoder. The inputs are:

• the path of the directory containing the post-process files;
• the path of the output directory;
• the maximum sentence length used during the training (longer sentences will not be used for training);
• number of maximum training iterations.

The script invokes the matlab code main.m. It logs the machine name and Matlab version. Then it preprocesses the data, sets the architecture, initializes the model, trains the model, and computes/saves the similarities among the sentences. The minFunc log is printed to ${ODIR}/logfile.log.

In addition to the log files, the program also saves the following files:

• data.mat contains the input data including the corpus, vocabulary (a 1-by-|V| cell array), and We (the m-by-|V| word embedding matrix where m is the size of the word vectors). So columns of We correspond to word embeddings.
• corpus.dist.matrix.mat contains the distance matrix saved as matlab file. The values in the distance matrix are doubles that represent the Euclidean distance between two sentences. In particular, the cell (i,j) contains the Euclidean distance between the i-th sentence (i.e., i-th line in corpus.src) and the j-th sentence in the corpus.
• corpus.dist.matrix.csv contains the distance matrix saved as .csv file.
• corpus.sentence_codes.mat contain the embeddings for each sentence in the corpus. The sentence_codes object contains the representations for sentences, and the pairwise Euclidean distance between these representations are used to measure similarity.
• detector.mat contains opttheta (the trained clone detector), hparams, and options.

The distance matrix can be used to sort sentences with respect to similarity in order to identify code clones.

Execution Example

The repository also contains input and output example data in data/ and out/ folders. The following lines of code perform the steps explained above and generated the output data.

cd bin/
./run_word2vec.sh ../data/ ../out/word2vec/ 100
./run_postprocess.py --w2v ../out/word2vec/word2vec.out  --src ../data/
cd rae/
./run_rae.sh ../../out/word2vec/ ../../out/rae/ 50 2

Bibliography

[1] Deep Learning Code Fragments for Code Clone Detection [paper, website]

@inproceedings{White:2016:DLC:2970276.2970326,
 author = {White, Martin and Tufano, Michele and Vendome, Christopher and Poshyvanyk, Denys},
 title = {Deep Learning Code Fragments for Code Clone Detection},
 booktitle = {Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering},
 series = {ASE 2016},
 year = {2016},
 isbn = {978-1-4503-3845-5},
 location = {Singapore, Singapore},
 pages = {87--98},
 numpages = {12},
 url = {http://doi.acm.org/10.1145/2970276.2970326},
 doi = {10.1145/2970276.2970326},
 acmid = {2970326},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {abstract syntax trees, code clone detection, deep learning, language models, machine learning, neural networks},
} 

[2] Deep Learning Similarities from Different Representations of Source Code [paper, website]

@inproceedings{Tufano:MSR:2018,
 author = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Di Penta, Massimiliano and White, Martin and Poshyvanyk, Denys},
 title = {Deep Learning Similarities from Different Representations of Source Code},
 booktitle = {Proceedings of the 15th International Conference on Mining Software Repositories},
 series = {MSR '18},
 year = {2018},
 isbn = {978-1-4503-5716-6/18/05},
 location = {Gothenburg, Sweden},
 url = {https://doi.org/10.1145/3196398.3196431},
 doi = {10.1145/3196398.3196431},
 publisher = {ACM},
 keywords = {deep learning, code similarities, neural networks}
}

[3] Efficient Estimation of Word Representations in Vector Space

@article{DBLP:journals/corr/abs-1301-3781,
  author    = {Tomas Mikolov and
               Kai Chen and
               Greg Corrado and
               Jeffrey Dean},
  title     = {Efficient Estimation of Word Representations in Vector Space},
  journal   = {CoRR},
  volume    = {abs/1301.3781},
  year      = {2013},
  url       = {http://arxiv.org/abs/1301.3781},
  archivePrefix = {arXiv},
  eprint    = {1301.3781},
  timestamp = {Wed, 07 Jun 2017 14:42:25 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1301-3781},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

[4] Semi-supervised recursive autoencoders for predicting sentiment distributions

@inproceedings{Socher:2011:SRA:2145432.2145450,
 author = {Socher, Richard and Pennington, Jeffrey and Huang, Eric H. and Ng, Andrew Y. and Manning, Christopher D.},
 title = {Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions},
 booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
 series = {EMNLP '11},
 year = {2011},
 isbn = {978-1-937284-11-4},
 location = {Edinburgh, United Kingdom},
 pages = {151--161},
 numpages = {11},
 url = {http://dl.acm.org/citation.cfm?id=2145432.2145450},
 acmid = {2145450},
 publisher = {Association for Computational Linguistics},
 address = {Stroudsburg, PA, USA},
} 

Acknowledgements

We gratefully acknowledge financial support from the NSF on this research project.