In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

We present a learning-based approach to detect and fix a broad range of bugs in Javascript
programs. We frame the problem in terms of learning a sequence of graph transformations …

Fuzzing and symbolic execution are two complementary techniques for discovering software
vulnerabilities. Fuzzing is fast and scalable, but can be ineffective when it fails to randomly …

Abstract Machine learning (ML) is increasingly seen as a viable approach for building
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …

Identifying invariants is an important program analysis task with applications towards
program understanding, bug finding, vulnerability analysis, and formal verification. Existing …

Type inference over partial contexts in dynamically typed languages is challenging. In this
work, we present a graph neural network model that predicts types by probabilistically …

Formal verification provides a rigorous and systematic approach to ensure the correctness
and reliability of software systems. Yet, constructing specifications for the full proof relies on …

We address the problem of teaching a deep reinforcement learning (RL) agent to follow
instructions in multi-task environments. Instructions are expressed in a well-known formal …