We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xz-x/gennm-ndss-ae | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| parse_answer | Ran | xz-x/gennm-ndss-ae/inference/infer_vllm.py code served (permissive licence) · get_code("1f5426ac10ec0aad") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Decompilation aims to recover the source code form of a binary executable. It has many security applications, such as malware analysis, vulnerability detection, and code hardening. A prominent challenge in decompilation is to recover variable names. We propose a novel technique that leverages the strengths of generative models while mitigating model biases. We build a prototype, GenNm, from pre-trained generative models CodeGemma-2B, CodeLlama-7B, and CodeLlama-34B. We finetune GenNm on decompiled functions and teach models to leverage contextual information. GenNm includes names from callers and callees while querying a function, providing rich contextual information within the model's input token limitation. We mitigate model biases by aligning the output distribution of models with symbol preferences of developers. Our results show that GenNm improves the state-of-the-art name recovery precision by 5.6-11.4 percentage points on two commonly used datasets and improves the state-of-the-art by 32% (from 17.3% to 22.8%) in the most challenging setup where ground-truth variable names are not seen in the training dataset.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2306.02546")
get_code_for_paper("2306.02546")
have("2306.02546")
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