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For complete reproduction, please go to README.md.

Ⅰ) Requirements for Reproduction

A) Environment

  • python 3.7 (Anaconda recommended)
  • pip install -r requirements.txt

run sudo apt-get install python3.7-dev first if you don't have python3.7 dev package.

B) Data elements

download ASE2022withTextUnique.zip (need to be unzipped) and ASE_features2_bert.pickle from data in Zenodo, accordingly change the absolute path of these two files in experiment/config.py of this repository as below.

  1. self.path_patch ---> ASE2022withTextUnique. Original dataset with patches text and commit messages text.
  2. self.path_ASE2020_feature ---> ASE_features2_bert.pickle. The feature from Tian et al.'s ASE2020 paper for our RQ3 DL experiment.

C) Test

Execute the following command to see if you can successfully obtain RQ1 results (Figure 6 & Table 2).

python run.py RQ1

Ⅱ) Custom Prediction

To predict the correctness of your custom patches, you are welcome to use the prediction interface.

A) Requirements

  • BERT model client&server: 24-layer, 1024-hidden, 16-heads, 340M parameters. download it here.

  • Environment for BERT server (different from reproduction)

    • python 3.7
    • pip install tensorflow==1.14
    • pip install bert-serving-client==1.10.0
    • pip install bert-serving-server==1.10.0
    • pip install protobuf==3.20.1
    • Launch BERT server via bert-serving-start -model_dir "Path2BertModel"/wwm_cased_L-24_H-1024_A-16 -num_worker=2 -max_seq_len=360 -port 8190
    • switch the port in BERT_Port in case your port 8190 is occupied.

  • Bug report text: developer-written bug report.

  • Patch description text: generating patch description for your plausible patches with commit message generation tools, e.g. CodeTrans. Github and API.

B) Predict

Let's give it a try!

python run.py predict $bug_report_text $patch_description_text

For instance: python run.py predict 'Missing type-checks for var_args notation' 'check var_args properly'