Reverse engineering binaries is required to understand and analyse programs
for which the source code is unavailable. Decompilers can transform the largely
unreadable binaries into a more readable source code-like representation.
However, reverse engineering is time-consuming, much of which is taken up by
labelling the functions with semantic information.
While the automated summarisation of decompiled code can help Reverse
Engineers understand and analyse binaries, current work mainly focuses on
summarising source code, and no suitable dataset exists for this task.
In this work, we extend large pre-trained language models of source code to
summarise decompiled binary functions. Furthermore, we investigate the impact
of input and data properties on the performance of such models. Our approach
consists of two main components; the data and the model.
We first build CAPYBARA, a dataset of 214K decompiled function-documentation
pairs across various compiler optimisations. We extend CAPYBARA further by
generating synthetic datasets and deduplicating the data.
Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5
achieves the state-of-the-art BLEU-4 score of 60.83, 58.82, and 44.21 for
summarising source, decompiled, and synthetically stripped decompiled code,
respectively. This indicates that these models can be extended to decompiled
binaries successfully.
Finally, we found that the performance of BinT5 is not heavily dependent on
the dataset size and compiler optimisation level. We recommend future research
to further investigate transferring knowledge when working with less expressive
input formats such as stripped binaries.

本文研究了利用大型预先训练的源代码模型来对反编译二进制文件的函数进行自动摘要，建立了一个包含多种编译器优化的数据集 CAPYBARA，进一步生成了合成数据集来优化模型性能，并创造了 BinT5 模型，成功地用于反汇编二进制代码中。

将源代码预训练语言模型扩展至汇编代码反编译摘要

Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries

We introduce the task of historical text summarisation, where documents in
historical forms of a language are summarised in the corresponding modern
language. This is a fundamentally important routine to historians and digital
humanities researchers but has never been automated. We compile a high-quality
gold-standard text summarisation dataset, which consists of historical German
and Chinese news from hundreds of years ago summarised in modern German or
Chinese. Based on cross-lingual transfer learning techniques, we propose a
summarisation model that can be trained even with no cross-lingual (historical
to modern) parallel data, and further benchmark it against state-of-the-art
algorithms. We report automatic and human evaluations that distinguish the
historic to modern language summarisation task from standard cross-lingual
summarisation (i.e., modern to modern language), highlight the distinctness and
value of our dataset, and demonstrate that our transfer learning approach
outperforms standard cross-lingual benchmarks on this task.

我们引入历史文本摘要的任务，其中历史形式的语言文档被概括为相应的现代语言。基于跨语言迁移学习技术，我们提出了一个摘要模型，可以即使没有跨语言（历史到现代）平行数据，也可以进行训练，并对最先进的算法进行基准测试。我们报告了自动和人工评估，突出了我们数据集的独特性和价值，并证明了我们的迁移学习方法在这项任务上优于标准的跨语言基准。