Datasets:
ArXiv:
License:
| from typing import List | |
| import os | |
| import glob | |
| import datasets | |
| _DESCRIPTION = """Given two codes as the input, the task is to do binary classification (0/1), where 1 stands for semantic equivalence and 0 for others.""" | |
| _CITATION = """@inproceedings{10.1145/3236024.3236068, | |
| author = {Zhao, Gang and Huang, Jeff}, | |
| title = {DeepSim: Deep Learning Code Functional Similarity}, | |
| year = {2018}, | |
| isbn = {9781450355735}, | |
| publisher = {Association for Computing Machinery}, | |
| address = {New York, NY, USA}, | |
| url = {https://doi.org/10.1145/3236024.3236068}, | |
| doi = {10.1145/3236024.3236068}, | |
| booktitle = {Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}, | |
| pages = {141–151}, | |
| numpages = {11}, | |
| keywords = {Classification, Control/Data flow, Code functional similarity, Deep Learning}, | |
| location = {Lake Buena Vista, FL, USA}, | |
| series = {ESEC/FSE 2018} | |
| } | |
| """ | |
| SPLITS = { | |
| 'test': [5, 6, 7, 8, 12], # For test in `Language Models are Universal Embedders` https://arxiv.org/pdf/2310.08232.pdf | |
| 'deepsim': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] | |
| } | |
| _URL = "https://huggingface.co/datasets/izhx/google-code-jam/resolve/main/googlejam4.tar.gz" | |
| class GoogleCodeJam(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig(name='default', version=datasets.Version("1.0.0"), description=_DESCRIPTION) | |
| ] | |
| DEFAULT_CONFIG_NAME = "default" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "fn1": datasets.Value("string"), | |
| "code1": datasets.Value("string"), | |
| "fn2": datasets.Value("string"), | |
| "code2": datasets.Value("string"), | |
| "label": datasets.Value("int32"), | |
| } | |
| ), | |
| homepage="https://github.com/parasol-aser/deepsim", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: | |
| folder = dl_manager.download_and_extract(_URL) | |
| folder = os.path.join(folder, 'googlejam4_src') | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"folder": folder, "problems": SPLITS["test"]}), | |
| datasets.SplitGenerator(name='deepsim', gen_kwargs={"folder": folder, "problems": SPLITS["deepsim"]}), | |
| ] | |
| def _generate_examples(self, folder, problems: list): | |
| raw = dict() | |
| for i in problems: | |
| group = list() | |
| for path in sorted(glob.glob(f'{folder}/{i}/*.java')): | |
| with open(path) as file: | |
| lines = [l for l in file] | |
| name = os.path.basename(path) | |
| group.append((name, ''.join(lines[1:]))) # remove name line | |
| raw[i] = group | |
| _id = 0 | |
| reverse = False | |
| for i in range(len(problems)): | |
| vi = raw[problems[i]] | |
| for n1, (fn1, code1) in enumerate(vi): | |
| for j in range(i, len(problems)): | |
| vj = raw[problems[j]] | |
| match = i == j | |
| for n2, (fn2, code2) in enumerate(vj): | |
| if match and n2 <= n1: | |
| continue | |
| ins = {'fn1': fn1, 'code1': code1, 'fn2': fn2, 'code2': code2, 'label': int(match)} | |
| if reverse: | |
| ins['fn1'], ins['fn2'] = ins['fn2'], ins['fn1'] | |
| ins['code1'], ins['code2'] = ins['code2'], ins['code1'] | |
| yield _id, ins | |
| _id += 1 | |
| reverse = not reverse | |