# coding=utf-8 # Copyright 2023 Charles Koutcheme and the original authors of the datasets # # 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. import json import datasets from itertools import product _DUBLIN_DESCRIPTION = """ The Dublin programming dataset is a dataset composed of students' submissions to introductory programming assignments at the University of Dublin. Students submitted these programs for multiple programming courses over the duration of three academic years.""" _SINGAPORE_DESCRIPTION = """ This dataset contains 2442 correct and 1783 buggy program attempts by 361 undergraduate students crediting an introduction to Python programming course at NUS (National University of Singapore). """ _NEW_CALEDONIA_DESCRIPTION = """ The NewCaledonia dataset includes the programs submitted in 2020 by a group of 60 students from the University of New Caledonia, on a programming training platform. This plateform were developed and made available by the Computer Science department from the Orléans' Technological Institute (University of Orléans, France). This release contains a subset of the assignments. """ _DUBLIN_HOMEPAGE = """https://figshare.com/articles/dataset/_5_Million_Python_Bash_Programming_Submissions_for_5_Courses_Grades_for_Computer-Based_Exams_over_3_academic_years_/12610958""" _SINGAPORE_HOMEPAGE = """https://github.com/githubhuyang/refactory""" _NEW_CALEDONIA_HOMEPAGE = """https://github.com/GCleuziou/code2aes2vec/tree/master/Datasets""" _DUBLIN_CITATION = """ @inproceedings{azcona2019user2code2vec, title={user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source Code}, author={Azcona, David and Arora, Piyush and Hsiao, I-Han and Smeaton, Alan}, booktitle={Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK’19)}, year={2019}, organization={ACM} } @inproceedings{DBLP:conf/edm/CleuziouF21, author = {Guillaume Cleuziou and Fr{\'{e}}d{\'{e}}ric Flouvat}, editor = {Sharon I{-}Han Hsiao and Shaghayegh (Sherry) Sahebi and Fran{\c{c}}ois Bouchet and Jill{-}J{\^{e}}nn Vie}, title = {Learning student program embeddings using abstract execution traces}, booktitle = {Proceedings of the 14th International Conference on Educational Data Mining, {EDM} 2021, virtual, June 29 - July 2, 2021}, publisher = {International Educational Data Mining Society}, year = {2021}, timestamp = {Wed, 09 Mar 2022 16:47:22 +0100}, biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _SINGAPORE_CITATION = """ @inproceedings{yang2019refactory, title={Re-factoring based Program Repair applied to Programming Assignments}, author={Hu, Yang and Ahmed, Umair Z. and Mechtaev, Sergey and Leong, Ben and Roychoudhury, Abhik}, booktitle={2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)}, pages={388--398}, year={2019}, organization={IEEE/ACM} } """ _NEW_CALEDONIA_CITATION = """ @inproceedings{DBLP:conf/edm/CleuziouF21, author = {Guillaume Cleuziou and Fr{\'{e}}d{\'{e}}ric Flouvat}, editor = {Sharon I{-}Han Hsiao and Shaghayegh (Sherry) Sahebi and Fran{\c{c}}ois Bouchet and Jill{-}J{\^{e}}nn Vie}, title = {Learning student program embeddings using abstract execution traces}, booktitle = {Proceedings of the 14th International Conference on Educational Data Mining, {EDM} 2021, virtual, June 29 - July 2, 2021}, publisher = {International Educational Data Mining Society}, year = {2021}, timestamp = {Wed, 09 Mar 2022 16:47:22 +0100}, biburl = {https://dblp.org/rec/conf/edm/CleuziouF21.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DESCRIPTION = """ Intro Programming. A dataset of student submissions to programming assignments. """ _DUBLIN_URLS = { "metadata": { "train": "./data/dublin_metadata_train.jsonl", "test": "./data/dublin_metadata_test.jsonl" }, "data": { "train": f"./data/dublin_data_train.jsonl", "test": f"./data/dublin_data_test.jsonl", }, "repair": { "train": f"./data/dublin_repair_train.jsonl", "test": f"./data/dublin_repair_test.jsonl", } } _SINGAPORE_URLS = { "metadata": { "train": "./data/singapore_metadata_train.jsonl", }, "data": { "train": f"./data/singapore_data_train.jsonl", }, "repair": { "train": f"./data/singapore_repair_train.jsonl", } } _NEW_CALEDONIA_URLS = { "metadata": { "train": "./data/newcaledonia_metadata_train.jsonl", }, "data": { "train": f"./data/newcaledonia_data_train.jsonl", }, } _URLS = { "dublin": _DUBLIN_URLS, "singapore": _SINGAPORE_URLS, "newcaledonia": _NEW_CALEDONIA_URLS, } class IntroProgConfig(datasets.BuilderConfig): """ BuilderConfig for IntroProg.""" def __init__(self, **kwargs): """BuilderConfig for IntroProg. Args: **kwargs: keyword arguments forwarded to super. """ super(IntroProgConfig, self).__init__(**kwargs) class IntroProg(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("2.12.0") # splits "data", "repair", "bugs" # also add here the "metadata" split which will also contain the full metadata tasks = [("metadata", "Information about the programming assignments."), ("data", "Submissions to the programming assignments."), ("repair", "Buggy programs and ground truth repair(s)."),] # ("bug", "Buggy programs and bug categories.")] sources = ["dublin", "singapore"] configurations = list(product(tasks, sources)) configurations.append((tasks[0], "newcaledonia")) configurations.append((tasks[1], "newcaledonia")) BUILDER_CONFIGS = [] for (task, description), source in configurations: BUILDER_CONFIGS.append( IntroProgConfig( name=f"{source}_{task}", version=VERSION, ) ) def _info(self): source, task = self.config.name.split("_") if source == "dublin": description = _DUBLIN_DESCRIPTION citation = _DUBLIN_CITATION homepage = _DUBLIN_HOMEPAGE elif source == "singapore": description =_SINGAPORE_DESCRIPTION citation = _SINGAPORE_CITATION homepage = _SINGAPORE_HOMEPAGE elif source == "newcaledonia": description = _NEW_CALEDONIA_DESCRIPTION citation = _NEW_CALEDONIA_CITATION homepage = _NEW_CALEDONIA_HOMEPAGE else: description = "" citation = "" homepage = "" main_features = datasets.Features({ "submission_id": datasets.Value("int32"), "func_code": datasets.Value("string"), # assignment information "assignment_id": datasets.Value("string"), "func_name": datasets.Value("string"), "description": datasets.Value(dtype='string'), "test": datasets.Value(dtype='string'), }) if task == "data": features = main_features features["correct"] = datasets.Value(dtype="bool") if source == "dublin": features["user"] = datasets.Value("string") features["academic_year"] = datasets.Value('int32') features['date']: datasets.Value('timestamp[s]') elif task == "metadata": # metadata information features = datasets.Features({ "assignment_id": datasets.Value("string"), "func_name": datasets.Value("string"), "reference_solution": datasets.Value("string"), "description": datasets.Value("string"), "test": datasets.Value("string"), }) elif task == "repair": features = main_features features["annotation"] = datasets.Value("string") if source == "dublin": features["user"] = datasets.Value("string") features["academic_year"] = datasets.Value('int32') features['date']: datasets.Value('timestamp[s]') elif task == "bug": features = main_features features["comments"] = datasets.Value("string") return datasets.DatasetInfo( description=description, citation=citation, homepage=homepage, features=features, supervised_keys=None, ) def _split_generators(self, dl_manager: datasets.DownloadManager): source, task = self.config.name.split("_") urls = _URLS[source][task] downloaded_files = dl_manager.download_and_extract(urls) splits = [] for name, files in downloaded_files.items(): splits.append(datasets.SplitGenerator(name=name, gen_kwargs={"filepath": files})) return splits def _generate_examples(self, filepath): with open(filepath, "r") as f: lines = f.read().splitlines() for key, line in enumerate(lines): d = json.loads(line) d = {k:v for k, v in d.items() if k in self.info.features} yield key, d