mBERT is a mutation testing tool that uses CodeBERT, a pre-trained language model, to generate mutants.
pip install torch
pip install transformers
Run mkdir -p pre-trained/codebert-base-mlm
to create the folder where CodeBERT pre-trained model will be saved.
Run python3 download-codebert.py
to download CodeBERT pre-trained model.
Try CodeBERT by running: python3 run-codebert.py "int <mask> = b;"
{'score': 0.23396340012550354, 'token': 740, 'token_str': 'c', 'sequence': 'int c= b;'}
{'score': 0.05450829118490219, 'token': 939, 'token_str': 'i', 'sequence': 'int i= b;'}
{'score': 0.05004948750138283, 'token': 741, 'token_str': 'b', 'sequence': 'int b= b;'}
{'score': 0.04164685681462288, 'token': 10, 'token_str': 'a', 'sequence': 'int a= b;'}
{'score': 0.023635799065232277, 'token': 181, 'token_str': 'p', 'sequence': 'int p= b;'}
Compile mBERT: mvn compile
.
Done! Try mBERT by running: ./mBERT.sh
mBERT provides some flags that you can configure:
-in=source_file_name
-out=mutants_directory
-N=max_num_of_mutants
-m=method_name
-m=method_name:method_definition_line
-l=line_to_mutate
You can use script compile-mutants.sh
to compile the mutants generated by mBERT.
./compile-mutants.sh mutants_dir subject_name
mutants_dir/subject_name.csv
and mutants_dir/subject_name.log
./compile-mutants.sh examples/generated-mutants/gcd/ gcd
Forlder examples
provide the examples discussed in the paper.
Inside examples/generated-mutants
you can find the mutants generated by mBERT.
If you use mBERT in your research, please cite our paper:
@inproceedings{DBLP:conf/icst/DegiovanniP22,
author = {Renzo Degiovanni and
Mike Papadakis},
title = {{\(\mathrm{\mu}\)}Bert: Mutation Testing using Pre-Trained Language
Models},
booktitle = {15th {IEEE} International Conference on Software Testing, Verification
and Validation Workshops {ICST} Workshops 2022, Valencia, Spain, April
4-13, 2022},
pages = {160--169},
publisher = {{IEEE}},
year = {2022},
url = {https://doi.org/10.1109/ICSTW55395.2022.00039},
doi = {10.1109/ICSTW55395.2022.00039},
timestamp = {Mon, 13 Jun 2022 16:53:37 +0200},
biburl = {https://dblp.org/rec/conf/icst/DegiovanniP22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}