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details of splitting exebench for train and evaluation #28
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We use all the training samples:
And test on its test set:
Good luck for your project! |
ok, i get it. thanks for your reply |
hi, I found that the exebench has three optimization options (O0, O3, Os). How can I evaluate your tool on different options (O0, O1, O2, O3) similar to your experiments? |
Unfortunately, you will have to compile the dataset on your own as we do not have the authorization to distribute another's dataset. For more details on the issues we faced, please refer to Appendix A in our paper. |
OK, how can i obtain the source code of exebench? i can not find them from huggingface. |
you can find it here |
I can not find the source code from this link. I just found the below files: train_not_compilable: 2.357M do you mean the source code is contained in these? |
that's all they provided... |
ok, I noticed that your paper tested the re-executability rate of the exebench dataset. Can I ask how you achieved it? |
in the examples/basic.py, you can see
it requires the func_assembly.
We made some additional changes to the code for our specific needs, but that's essentially how you can modify it. However, we were only able to compile half of the code with these modifications. |
hello, i m impressed by the Decompile model you released.
i want to know the details of splitting exebench for train and evaluation. because i want to reproduce your evaluation results for a better application.
thank you.
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