Update exebench.py

exebench.py

@@ -20,7 +20,22 @@ from pathlib import Path
 
 
 _CITATION = """\
-@misc{TODO
+@inproceedings{10.1145/3520312.3534867,
+author = {Armengol-Estap\'{e}, Jordi and Woodruff, Jackson and Brauckmann, Alexander and Magalh\~{a}es, Jos\'{e} Wesley de Souza and O'Boyle, Michael F. P.},
+title = {ExeBench: An ML-Scale Dataset of Executable C Functions},
+year = {2022},
+isbn = {9781450392730},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3520312.3534867},
+doi = {10.1145/3520312.3534867},
+abstract = {Machine-learning promises to transform compilation and software engineering, yet is frequently limited by the scope of available datasets. In particular, there is a lack of runnable, real-world datasets required for a range of tasks ranging from neural program synthesis to machine learning-guided program optimization. We introduce a new dataset, ExeBench, which attempts to address this. It tackles two key issues with real-world code: references to external types and functions and scalable generation of IO examples. ExeBench is the first publicly available dataset that pairs real-world C code taken from GitHub with IO examples that allow these programs to be run. We develop a toolchain that scrapes GitHub, analyzes the code, and generates runnable snippets of code. We analyze our benchmark suite using several metrics, and show it is representative of real-world code. ExeBench contains 4.5M compilable and 700k executable C functions. This scale of executable, real functions will enable the next generation of machine learning-based programming tasks.},
+booktitle = {Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming},
+pages = {50–59},
+numpages = {10},
+keywords = {Code Dataset, Program Synthesis, Mining Software Repositories, C, Machine Learning for Code, Compilers},
+location = {San Diego, CA, USA},
+series = {MAPS 2022}
 }
 """
 
@@ -126,36 +141,36 @@ class ExeBench(datasets.GeneratorBasedBuilder):
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
         urls_to_download = {
-                # "train_not_compilable": f"{_URL}train_not_compilable.tar.gz",
-                #"train_synth_compilable": f"{_URL}train_synth_compilable.tar.gz",
-                # "train_real_compilable": f"{_URL}train_real_compilable.tar.gz",
-                #"train_synth_simple_io": f"{_URL}train_synth_simple_io.tar.gz",
-                # "train_real_simple_io": f"{_URL}train_real_simple_io.tar.gz",
-                #"train_synth_rich_io": f"{_URL}train_synth_rich_io.tar.gz",
-                #"valid_synth": f"{_URL}valid_synth.tar.gz",
-                # "valid_real": f"{_URL}valid_real.tar.gz",
+                "train_not_compilable": f"{_URL}train_not_compilable.tar.gz",
+                "train_synth_compilable": f"{_URL}train_synth_compilable.tar.gz",
+                "train_real_compilable": f"{_URL}train_real_compilable.tar.gz",
+                "train_synth_simple_io": f"{_URL}train_synth_simple_io.tar.gz",
+                "train_real_simple_io": f"{_URL}train_real_simple_io.tar.gz",
+                "train_synth_rich_io": f"{_URL}train_synth_rich_io.tar.gz",
+                "valid_synth": f"{_URL}valid_synth.tar.gz",
+                "valid_real": f"{_URL}valid_real.tar.gz",
                 "test_synth": f"{_URL}test_synth.tar.gz",
                 "test_real": f"{_URL}test_real.tar.gz",
              }
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         return [
-            #datasets.SplitGenerator(name='train_not_compilable',
-            #                        gen_kwargs={"files": downloaded_files["train_not_compilable"]}),
-            #datasets.SplitGenerator(name='train_synth_compilable',
-            #                       gen_kwargs={"files": downloaded_files["train_synth_compilable"]}),
-            #datasets.SplitGenerator(name='train_real_compilable',
-            #                        gen_kwargs={"files": downloaded_files["train_real_compilable"]}),
-            #datasets.SplitGenerator(name='train_synth_simple_io',
-            #                        gen_kwargs={"files": downloaded_files["train_synth_simple_io"]}),
-            #datasets.SplitGenerator(name='train_real_simple_io',
-            #                        gen_kwargs={"files": downloaded_files["train_real_simple_io"]}),
-            #datasets.SplitGenerator(name='train_synth_rich_io',
-            #                        gen_kwargs={"files": downloaded_files["train_synth_rich_io"]}),
-            #datasets.SplitGenerator(name='valid_synth',
-            #                       gen_kwargs={"files": downloaded_files["valid_synth"]}),
-            #datasets.SplitGenerator(name='valid_real',
-            #                        gen_kwargs={"files": downloaded_files["valid_real"]}),
+            datasets.SplitGenerator(name='train_not_compilable',
+                                    gen_kwargs={"files": downloaded_files["train_not_compilable"]}),
+            datasets.SplitGenerator(name='train_synth_compilable',
+                                   gen_kwargs={"files": downloaded_files["train_synth_compilable"]}),
+            datasets.SplitGenerator(name='train_real_compilable',
+                                    gen_kwargs={"files": downloaded_files["train_real_compilable"]}),
+            datasets.SplitGenerator(name='train_synth_simple_io',
+                                    gen_kwargs={"files": downloaded_files["train_synth_simple_io"]}),
+            datasets.SplitGenerator(name='train_real_simple_io',
+                                    gen_kwargs={"files": downloaded_files["train_real_simple_io"]}),
+            datasets.SplitGenerator(name='train_synth_rich_io',
+                                    gen_kwargs={"files": downloaded_files["train_synth_rich_io"]}),
+            datasets.SplitGenerator(name='valid_synth',
+                                   gen_kwargs={"files": downloaded_files["valid_synth"]}),
+            datasets.SplitGenerator(name='valid_real',
+                                    gen_kwargs={"files": downloaded_files["valid_real"]}),
             datasets.SplitGenerator(name='test_synth',
                                     gen_kwargs={"files": downloaded_files["test_synth"]}),
             datasets.SplitGenerator(name='test_real',