English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Reverse engineering binaries is required to understand and analyse programsfor which the source code is unavailable. Decompilers can transform the largelyunreadable binaries into a more readable source code-like representation.However, reverse engineering is time-consuming, much of which is taken up bylabelling the functions with semantic information.  While the automated summarisation of decompiled code can help ReverseEngineers understand and analyse binaries, current work mainly focuses onsummarising source code, and no suitable dataset exists for this task.  In this work, we extend large pre-trained language models of source code tosummarise decompiled binary functions. Furthermore, we investigate the impactof input and data properties on the performance of such models. Our approachconsists of two main components; the data and the model.  We first build CAPYBARA, a dataset of 214K decompiled function-documentationpairs across various compiler optimisations. We extend CAPYBARA further bygenerating synthetic datasets and deduplicating the data.  Next, we fine-tune the CodeT5 base model with CAPYBARA to create BinT5. BinT5achieves the state-of-the-art BLEU-4 score of 60.83, 58.82, and 44.21 forsummarising source, decompiled, and synthetically stripped decompiled code,respectively. This indicates that these models can be extended to decompiledbinaries successfully.  Finally, we found that the performance of BinT5 is not heavily dependent onthe dataset size and compiler optimisation level. We recommend future researchto further investigate transferring knowledge when working with less expressiveinput formats such as stripped binaries.

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