DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode

Understand

The automation of a large number of software engineering tasks is becoming possible thanks to Machine Learning (ML).

• Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
• Traditionally, researchers have relied on manually selected features, based on expert knowledge which is sometimes imprecise and generally incomplete.
• Representation learning has allowed ML to automatically choose suitable representations and relevant features.