Instein98 / NPR4J

NPR4J-Framework

Features

Overall Procedure

Install Prerequirement

pip install bson pymongo h5py javalang nltk torch transformers

Data Preprocess

Processing data into forms that each NPR system needs

Preprocess_RawData.py

Before data preprocessing, you need to prepare your data into a filedir including:

|---data_dir
   |---data.ids: each line has a id to identify data samples
   |---buggy_lines: each file contains the buggy line of a sample
   |---buggy_methods: each file contains the buggy method of a sample
   |---buggy_classes: each file contains the buggy class of a sample
   |---fix_lines: each file contains the developer patch line of a sample
   |---fix_methods: each file contains the developer patch method of a sample
   |---fix_classes: each file contains the developer patch class of a sample
   |---metas: meta information of data samples

Raw data of NPR4J-Benchmark can be downloaded from this link: https://drive.google.com/drive/folders/1vKyABQbdvH8SuQc23VihB2INj_brrdnv?usp=sharing

Training

To train a NPR system, you can use a simple command like this:

python train.py -model NPR_SYSTEM_NAME -config CONFIG_FILE_PATH

Generating Patches

To use a trained NPR system to generate patches, you can use a simple command like this:

python translate.py -model NPR_SYSTEM_NAME -config CONFIG_FILE_PATH

Resources of trained NPR systems

Trained NPR models can be downloaded from this link: https://drive.google.com/drive/folders/18WmVJQwAOmcbudgHK839KYfY98JKVrEH?usp=sharing

GPU memory requirements for each NPR model (with tuned hyperparameters in our experiment)

SequenceR: 20GB for training, less than 10GB for predicting
Recoder: 40GB for training, 20GB for predicting
CODIT: less than 10GB for training and predicting
Edits: less than 10GB for training and predicting
CoCoNut (singleton mode): less than 10GB for training and predicting
Tufano: less than 10GB for trainging and predicting

Experiment Results for Standup4NPR (accepted by ASE 22)

1. Evaluation_Results.zip: concrete evaluation results of every candidate predicted by the six NPR systems.
   |---Evaluation_Result
      |---Diversity
         |---Main
         |---Bugs.jar
      |---Empirical
         |---Defects4J
         |---Bears
         |---QuixBugs
         |---Manual_Check: manual check results of plausible/correct patches

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