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updated names again

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  1. shellcode_i_a32.py +219 -0
shellcode_i_a32.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """TODO: Add a description here."""
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+
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+
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+ import csv
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+ import json
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+ import os
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+ import pandas as pd
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+ import datasets
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+
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+
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+ # TODO: Add BibTeX citation
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+ # Find for instance the citation on arxiv or on the dataset repo/website
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+ _CITATION = """\
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+ @inproceedings{liguori-etal-2021-shellcode,
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+ title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
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+ author = "Liguori, Pietro and
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+ Al-Hossami, Erfan and
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+ Cotroneo, Domenico and
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+ Natella, Roberto and
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+ Cukic, Bojan and
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+ Shaikh, Samira",
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+ booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
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+ month = aug,
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+ year = "2021",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2021.nlp4prog-1.7",
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+ doi = "10.18653/v1/2021.nlp4prog-1.7",
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+ pages = "58--64",
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+ abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
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+ }
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+ """
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+
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+ # TODO: Add description of the dataset here
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+ # You can copy an official description
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+ _DESCRIPTION = """\
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+ Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.
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+ """
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+
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+ # TODO: Add a link to an official homepage for the dataset here
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+ _HOMEPAGE = "https://github.com/dessertlab/Shellcode_IA32"
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+
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+ # TODO: Add the licence for the dataset here if you can find it
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+ _LICENSE = "GNU GENERAL PUBLIC LICENSE"
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+
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+ # TODO: Add link to the official dataset URLs here
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+ # The HuggingFace dataset library don't host the datasets but only point to the original files
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+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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+ _URLs = {
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+ 'default': "https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv",
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+ }
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+
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+
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+ # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
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+ class ShellcodeIA32(datasets.GeneratorBasedBuilder):
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+ """Shellcode_IA32 a dataset for shellcode generation"""
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+
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+ VERSION = datasets.Version("1.1.0")
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+
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+ # This is an example of a dataset with multiple configurations.
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+ # If you don't want/need to define several sub-sets in your dataset,
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+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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+ # If you need to make complex sub-parts in the datasets with configurable options
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+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
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+
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+ # You will be able to load one or the other configurations in the following list with
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+ # data = datasets.load_dataset('my_dataset', 'first_domain')
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+ # data = datasets.load_dataset('my_dataset', 'second_domain')
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+ # BUILDER_CONFIGS = [
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+ # datasets.BuilderConfig(name="default", version=VERSION, description="This part of my dataset covers the default train/test split"),
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+ # #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
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+ # ]
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+
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+ DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.
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+
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+ def _info(self):
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+ # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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+
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+ features = datasets.Features(
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+ {
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+ "intent": datasets.Value("string"),
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+ "snippet": datasets.Value("string"),
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+
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ # Citation for the dataset
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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+ # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
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+ # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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+ # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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+ my_urls = _URLs[self.config.name]
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+ data_dir = dl_manager.download_and_extract(my_urls)
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+ # return [
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+ # datasets.SplitGenerator(
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+ # name=datasets.Split.TRAIN,
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+ # # These kwargs will be passed to _generate_examples
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+ # gen_kwargs={
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+ # "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
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+ # "split": "train",
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+ # },
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+ # ),
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+ # datasets.SplitGenerator(
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+ # name=datasets.Split.TEST,
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+ # # These kwargs will be passed to _generate_examples
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+ # gen_kwargs={
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+ # "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
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+ # "split": "test"
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+ # },
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+ # ),
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+ # datasets.SplitGenerator(
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+ # name=datasets.Split.VALIDATION,
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+ # # These kwargs will be passed to _generate_examples
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+ # gen_kwargs={
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+ # "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
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+ # "split": "dev",
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+ # },
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+ # ),
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+ # ]
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir),
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+ "split": "train",
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir),
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+ "split": "test"
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": os.path.join(data_dir),
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+ "split": "dev",
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(
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+ self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ ):
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is here for legacy reason (tfds) and is not important in itself.
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+ """This function returns the examples in the raw (text) form."""
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+
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+ df = pd.read_csv(filepath, delimiter = '\t')
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+ train = df.sample(frac = 0.8, random_state = 0)
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+ test = df.drop(train.index)
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+ dev = test.sample(frac = 0.5, random_state = 0)
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+ test = test.drop(dev.index)
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+
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+ if split == 'train':
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+ data = train
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+ elif split == 'dev':
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+ data = dev
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+ elif split == 'test':
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+ data = test
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+
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+ for idx, row in data.iterrows():
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+ yield idx, {
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+ "snippet": row["SNIPPETS"],
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+ "intent": row["INTENTS"],
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+
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+ }
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+ # with open(filepath, encoding="utf-8") as f:
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+ # reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
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+ # reader =
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+ # for idx, row in enumerate(reader):
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+ #
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+ # yield idx, {
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+ # "snippet": row["SNIPPETS"],
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+ # "intent": row["INTENTS"],
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+ #
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+ # }