A Framework for Manipulating Existing Datasets of Security Patches for Automatic Program Repair Techniques and Studies

• Python (=>3.7)

• Pandas (>=1.0.3)
• Github

The baseline usage involves three operations: collect, transform and filter.

Collect from the specified source the dataset. The dataset is downloaded to the folder PatchBundle/data/collected/'name_of_the_dataset'

$ ./tool/PatchBundle.py collect --datasets nvd, mozilla, secretpatch, secbench

Transforms the collected dataset's records into the PatchRecord format. The dataset is saved in the folder PatchBundle/data/transformed/'name_of_the_dataset'

$ ./tool/PatchBundle.py transform --datasets nvd, mozilla, secretpatch, secbench

Filters the transformed dataset's based on pre-defined decorators in the file PatchBundle/tool/decorators/filter.py.

$ ./tool/PatchBundle.py filter --datasets nvd, mozilla, secretpatch, secbench

Implement your own filter rules and update the filter method in the respective dataset file, PatchBundle/tool/datasets/'name_of_the_dataset'.py.

Example of a custom filter:

def custom(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        dataset = func(*args, **kwargs)
        return dataset.drop(columns=['commit', 'name'])
    return wrapper

Add the filter:

    @save
--> @custom
    @one_line_changes
    @equal_adds_dels
    @c_code
    @load
    def filter(self, path: Path):
        print(f"Filtering {self.name}")
        return self.transformed