Datasets:

google
/

code_x_glue_cc_defect_detection

	from typing import List

	import datasets

	from .common import TrainValidTestChild
	from .generated_definitions import DEFINITIONS


	_DESCRIPTION = """Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
	The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test."""
	_CITATION = """@inproceedings{zhou2019devign,
	title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
	author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
	booktitle={Advances in Neural Information Processing Systems},
	pages={10197--10207}, year={2019}"""


	class CodeXGlueCcDefectDetectionImpl(TrainValidTestChild):
	_DESCRIPTION = _DESCRIPTION
	_CITATION = _CITATION

	_FEATURES = {
	"id": datasets.Value("int32"), # Index of the sample
	"func": datasets.Value("string"), # The source code
	"target": datasets.Value("bool"), # 0 or 1 (vulnerability or not)
	"project": datasets.Value("string"), # Original project that contains this code
	"commit_id": datasets.Value("string"), # Commit identifier in the original project
	}
	_SUPERVISED_KEYS = ["target"]

	def generate_urls(self, split_name):
	yield "index", f"{split_name}.txt"
	yield "data", "function.json"

	def _generate_examples(self, split_name, file_paths):
	import json

	js_all = json.load(open(file_paths["data"], encoding="utf-8"))

	index = set()
	with open(file_paths["index"], encoding="utf-8") as f:
	for line in f:
	line = line.strip()
	index.add(int(line))

	for idx, js in enumerate(js_all):
	if idx in index:
	js["id"] = idx
	js["target"] = int(js["target"]) == 1
	yield idx, js


	CLASS_MAPPING = {
	"CodeXGlueCcDefectDetection": CodeXGlueCcDefectDetectionImpl,
	}


	class CodeXGlueCcDefectDetection(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIG_CLASS = datasets.BuilderConfig
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
	]

	def _info(self):
	name = self.config.name
	info = DEFINITIONS[name]
	if info["class_name"] in CLASS_MAPPING:
	self.child = CLASS_MAPPING[info["class_name"]](info)
	else:
	raise RuntimeError(f"Unknown python class for dataset configuration {name}")
	ret = self.child._info()
	return ret

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	return self.child._split_generators(dl_manager=dl_manager)

	def _generate_examples(self, split_name, file_paths):
	return self.child._generate_examples(split_name, file_paths)