Comments (1)
DIRTY only handles variable renaming and retyping, and recovering the code tokens for target side (debug version) is not part of the task.
Therefore, the only information we need from the target side to construct the learning task is its variables. The rest of the information in the debug version including other code tokens can't be used since they're not available during inference.
In preprocessing, for each variable in the source side (striped version), we try to match its target (corresponding variable in debug version).
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Related Issues (14)
- Need for local saving and loading of model. HOT 1
- Dataset is deprecated HOT 1
- Hang when evaluating test set HOT 3
- Running tox command HOT 1
- Obtaining the Dataset HOT 5
- Serialization of Unions uses wrong type tag
- A typo in model.py?
- How can I apply `DIRTY` on .idb file?
- What are the "disappear" types? HOT 2
- [???] encode_memory() dirtiness HOT 5
- Help Needed/Documentation Inquiries HOT 2
- DIRTY_light access request
- Question on applying Dirty to custom binary files
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