The automation of an increasingly large number of software engineering tasks
is becoming possible thanks to Machine Learning (ML). One foundational building
block in the application of ML to software artifacts is the representation of
these artifacts (e.g., source code or executable code) into a form that is
suitable for learning. Many studies have leveraged representation learning,
delegating to ML itself the job of automatically devising suitable
representations. Yet, in the context of Android problems, existing models are
either limited to coarse-grained whole-app level (e.g., apk2vec) or conducted
for one specific downstream task (e.g., smali2vec). Our work is part of a new
line of research that investigates effective, task-agnostic, and fine-grained
universal representations of bytecode to mitigate both of these two
limitations. Such representations aim to capture information relevant to
various low-level downstream tasks (e.g., at the class-level). We are inspired
by the field of Natural Language Processing, where the problem of universal
representation was addressed by building Universal Language Models, such as
BERT, whose goal is to capture abstract semantic information about sentences,
in a way that is reusable for a variety of tasks. We propose DexBERT, a
BERT-like Language Model dedicated to representing chunks of DEX bytecode, the
main binary format used in Android applications. We empirically assess whether
DexBERT is able to model the DEX language and evaluate the suitability of our
model in two distinct class-level software engineering tasks: Malicious Code
Localization and Defect Prediction. We also experiment with strategies to deal
with the problem of catering to apps having vastly different sizes, and we
demonstrate one example of using our technique to investigate what information
is relevant to a given task.

我们提出了 DexBERT，这是一个类似于 BERT 的语言模型，专门用于表示 Android 应用程序中使用的主要二进制格式 DEX 字节码，以提高在两个不同的基于类级别的软件工程任务中的实用性：恶意代码本地化和缺陷预测。