Datasets:

SoLID
/

shellcode_i_a32

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""TODO: Add a description here."""


	import csv
	import json
	import os
	import pandas as pd
	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{liguori-etal-2021-shellcode,
	title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
	author = "Liguori, Pietro and
	Al-Hossami, Erfan and
	Cotroneo, Domenico and
	Natella, Roberto and
	Cukic, Bojan and
	Shaikh, Samira",
	booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
	month = aug,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.nlp4prog-1.7",
	doi = "10.18653/v1/2021.nlp4prog-1.7",
	pages = "58--64",
	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.",
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://github.com/dessertlab/Shellcode_IA32"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "GNU GENERAL PUBLIC LICENSE"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	'default': "https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv",
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class ShellcodeIA32(datasets.GeneratorBasedBuilder):
	"""Shellcode_IA32 a dataset for shellcode generation"""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	# BUILDER_CONFIGS = [
	# datasets.BuilderConfig(name="default", version=VERSION, description="This part of my dataset covers the default train/test split"),
	# #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
	# ]

	DEFAULT_CONFIG_NAME = "default" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

	features = datasets.Features(
	{
	"intent": datasets.Value("string"),
	"snippet": datasets.Value("string"),

	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# 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.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	# return [
	# datasets.SplitGenerator(
	# name=datasets.Split.TRAIN,
	# # These kwargs will be passed to _generate_examples
	# gen_kwargs={
	# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
	# "split": "train",
	# },
	# ),
	# datasets.SplitGenerator(
	# name=datasets.Split.TEST,
	# # These kwargs will be passed to _generate_examples
	# gen_kwargs={
	# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
	# "split": "test"
	# },
	# ),
	# datasets.SplitGenerator(
	# name=datasets.Split.VALIDATION,
	# # These kwargs will be passed to _generate_examples
	# gen_kwargs={
	# "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
	# "split": "dev",
	# },
	# ),
	# ]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir),
	"split": "dev",
	},
	),
	]

	def _generate_examples(
	self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	""" Yields examples as (key, example) tuples. """
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.
	"""This function returns the examples in the raw (text) form."""

	df = pd.read_csv(filepath, delimiter = '\t')
	train = df.sample(frac = 0.8, random_state = 0)
	test = df.drop(train.index)
	dev = test.sample(frac = 0.5, random_state = 0)
	test = test.drop(dev.index)

	if split == 'train':
	data = train
	elif split == 'dev':
	data = dev
	elif split == 'test':
	data = test

	for idx, row in data.iterrows():
	yield idx, {
	"snippet": row["SNIPPETS"],
	"intent": row["INTENTS"],

	}
	# with open(filepath, encoding="utf-8") as f:
	# reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
	# reader =
	# for idx, row in enumerate(reader):
	#
	# yield idx, {
	# "snippet": row["SNIPPETS"],
	# "intent": row["INTENTS"],
	#
	# }