xing-hu / re2com-opensource

Re2Com

Requirements

• Python 3.7
• TensorFlow 1.15
• CUDA 10.1
• NLTK 3.4.5
• Java 1.8.0

Train and Test

Our code is based on

• put the dataset into the "funcom" directory
  • the dataset can be found at https://s3.us-east-2.amazonaws.com/icse2018/funcom.tar.gz

    tar xvf funcom.tar.gz

• Process the dataset and generate the vocabulary

    ./data-process.py

• Build the Retrieval corpus

    cd retrieve
    ./compile.sh
    ./buildIndex.sh

• Generate exemplars for training and testing

    cd retrieve
    ./buildExemplars.sh

• Train Re2Com model for standard dataset

    cd standard
    python __main__.py re2com.yaml --train -v

• Train Re2Com model for challenge dataset

    cd challenge
    python __main__.py challenge.yaml --train -v

• Retrieve code with standard dataset as corpus
  • Note that the output file will only contain numbers, which are the line numbers of the retrieved code.

    cd retrieve
    ./search.sh standard-corpus ${input-file} ${output-file}

Evaluation

The evaluation results are located at standard/models/eval/ or challenge/models/eval/. Evaluation code is based on https://github.com/tylin/coco-caption

    cd evaluation
    python2 evaluate.py