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Naturalness of Hardware Descriptions

This repository contains our data used to measure naturalness of hardware descriptions and models for assignment completion. Details can be found in our ESEC/FSE'20 paper "On the Naturalness of Hardware Descriptions".

If you have used our data or code in a research project, please cite the research paper in any related publication:

@inproceedings{LeeETAL20HDLP,
  author =       {Jaeseong Lee and Pengyu Nie and Junyi Jessy Li and Milos Gligoric},
  title =        {On the Naturalness of Hardware Descriptions},
  booktitle =    {Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  pages =        {530--542},
  year =         {2020},
}

Data

Our data is located in data/. Its five sub-directories, data/vhdl, data/verilog, data/systemverilog, data/java-popular, and data/java-naturalness holds the data for each corpus. In the remainder of this file, I will use $lang to denote any of the five corpora. Under data/$lang, there is an ALL sub-directory which holds the data relevant for the corpus as a whole. The remaining sub-directories contain the data relevant for individual repositories: a sub-directory with name user_repo contains the data relevant to the repository https://github.com/user/repo. I will use $repo to denote any repository (excluding ALL).

  • data/$lang/repositories.txt: List of repositories in each corpus. On each line of this file, there is a URL to the GitHub repository, then a space, and then the SHA (revision) we used in our study. Due to license restrictions and size limitations, we cannot directly share all the files we analyzed, but they are accessible via the URLs and SHAs we provide.

  • data/$lang/ALL/cksum.txt: List of non-duplicate parsable files in each corpus. On each line of this file, there is a checksum (obtained by using cksum command), then a space, then the path of that file (after removing the _downloads/$lang/repos/ prefix).

  • data/$lang/$repo/cksum.txt: List of non-duplicate parsable files in each repository. Same format as data/$lang/ALL/cksum.txt.

  • data/$lang/$repo/num-lines.txt: Number of lines of code in each repository.

  • data/$lang/ALL/ce/: The results of naturalness experiments on this corpus. This directory contains order-$n.json files where $n is in {1, ..., 10}. Each order-$n.json file contains the cross entropies obtained on the 10 folds using n-gram language model.

  • data/$lang/$repo/ce/: The results of naturalness experiments on this corpus. Same format as data/$lang/ALL/ce/.

  • data/vhdl/ALL/assignments.json: All concurrent assignments in VHDL.

Assignment Completion Models

Based on our collected data, we build deep learning models for predicting the right hand side of concurrent assignments in VHDL. The code for our models is located at completion/.

Requirements

  • Python>=3.7.6

  • PyTorch==1.1.0 (see https://pytorch.org/get-started/previous-versions/#v110 for installation instructions)

  • Other Python package requirements listed at completion/requirements.txt. To install them, run the following command after installing Python and Pytorch:

# cd completion
pip install -r requirements.txt

Our code is partially based on the OpenNMT framework, whose license is at completion/LICENSE.md.

Steps to reproduce

To reproduce our experiments (training and testing the models), run this command to split the concurrent assignments dataset (data/vhdl/ALL/assignments.json):

# cd data
python -m hdlp.main split_dataset --cross-file --random-seed=27

Then, the commands to train and test each model are listed below (all commands should be executed under the completion directory). In the comment before each command, we also note down the time for training each model on our machine (Intel i7-8700 CPU @ 3.20GHz with 6 cores, 64GB RAM, Nvidia Geforce GTX 1080, Ubuntu 18.04). After training each model, their results can be found at completion/tests/$model where $model is the name of the model after removing parentheses.

  • S2S
# 5 hours
python3 ex_ms2.py --mode train --feat l --save_dir "S2S"
  • S2S-PA(1)
# 6 hours
python3 ex_ms2.py --mode train --feat lpa --save_dir "S2S-PA1" --pa_index 1
  • S2S-PA(1)+Type
# 12 hours
python3 ex_ms2.py --mode train --feat lpa+typeappend --save_dir "S2S-PA1+Type"
  • S2S-PA(2)+Type
# 12 hours
python3 ex_ms2.py --mode train --feat lpa+typeappend --pa_index 2 --save_dir "S2S-PA2+Type"
  • S2S-PA(3)+Type
# 12 hours
python3 ex_ms2.py --mode train --feat lpa+typeappend --pa_index 3 --save_dir "S2S-PA3+Type"
  • S2S-PA(4)+Type
# 12 hours
python3 ex_ms2.py --mode train --feat lpa+typeappend --pa_index 4 --save_dir "S2S-PA4+Type"
  • S2S-PA(5)+Type
# 12 hours
python3 ex_ms2.py --mode train --feat lpa+typeappend --pa_index 5 --save_dir "S2S-PA5+Type"
  • S2S-PA(Ensemb-1-5)+Type

Require first training S2S-PA(1)+Type, S2S-PA(2)+Type, S2S-PA(3)+Type, S2S-PA(4)+Type, S2S-PA(5)+Type.

# several minutes
python3 ex_ms2.py --mode testval --feat lpa+typeappend --save_dir "S2S-PA1+Type"
python3 ex_ms2.py --mode testval --feat lpa+typeappend --pa_index 2 --save_dir "S2S-PA2+Type"
python3 ex_ms2.py --mode testval --feat lpa+typeappend --pa_index 3 --save_dir "S2S-PA3+Type"
python3 ex_ms2.py --mode testval --feat lpa+typeappend --pa_index 4 --save_dir "S2S-PA4+Type"
python3 ex_ms2.py --mode testval --feat lpa+typeappend --pa_index 5 --save_dir "S2S-PA5+Type"
python3 ex_ms2.py --mode assemble --save_dir "S2S-PAEnsemb-1-5+Type" --which "S2S-PA1+Type" "S2S-PA2+Type" "S2S-PA3+Type" "S2S-PA4+Type" "S2S-PA5+Type"
  • S2S-PA(1-2)+Type
# 25 hours
python3 ex_msap.py --mode train --feat apa+typeappend --num_pa 2 --save_dir "S2S-PA1-2+Type"
  • S2S-PA(1-3)+Type
# 35 hours
python3 ex_msap.py --mode train --feat apa+typeappend --num_pa 3 --save_dir "S2S-PA1-3+Type"
  • S2S-PA(1-4)+Type
# 46 hours
python3 ex_msap.py --mode train --feat apa+typeappend --num_pa 4 --save_dir "S2S-PA1-4+Type"
  • S2S-PA(1-5)+Type
# 56 hours
python3 ex_msap.py --mode train --feat apa+typeappend --num_pa 5 --save_dir "S2S-PA1-5+Type"
  • S2S-PA(Concat-1-5)+Type
# 48 hours
python3 ex_s2s.py --mode train --num_pa=5 --type_append --save_dir "S2S-LHS+PAConcat-1-5+Type"
  • Rule-based baseline

Require first running S2S-PA(1) that performs necessary data processing.

# < 1 minute
python3 ex_baseline.py --ref_modelname "S2S-PA1" --modelname "Baseline"
  • 10gramLM

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# < 1 minute
python3 ex_ngram.py --order 10 --pa 0 --ref_modelname "S2S-PA1-5+Type" --modelname "10gramLM"
  • 10gramLM+PA(1)

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# < 1 minute
python3 ex_ngram.py --order 10 --pa 1 --ref_modelname "S2S-PA1-5+Type" --modelname "10gramLM+PA1"
  • 10gramLM+PA(1-5)

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# 5 minutes
python3 ex_ngram.py --order 10 --pa 5 --ref_modelname "S2S-PA1-5+Type" --modelname "10gramLM+PA1-5"
  • RNNLM

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# 10 minutes
python3 ex_ngram.py --pa 0 --rnn True --ref_modelname "S2S-PA1-5+Type" --modelname "RNNLM"
  • RNNLM+PA(1)

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# 20 minutes
python3 ex_ngram.py --pa 1 --rnn True --ref_modelname "S2S-PA1-5+Type" --modelname "RNNLM+PA1"
  • RNNLM+PA(1-5)

Require first running S2S-PA(1-5)+Type that performs necessary data processing.

# 30 minutes
python3 ex_ngram.py --pa 5 --rnn True --ref_modelname "S2S-PA1-5+Type" --modelname "RNNLM+PA1-5"

Other Code

The code directory contains some miscellaneous source code used in our experiments, described as follows. We found and adapted them from other open-source repositories, and would like to share them to facilitate future work.

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Code and data for "On the Naturalness of Hardware Descriptions" in ESEC/FSE'20

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