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 …

Large Language Models (LLMs) applied to code-related applications have emerged as a
prominent field, attracting significant interest from both academia and industry. However, as …

We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a
benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an …

In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

Code completion is one of the most useful features in the Integrated Development
Environments (IDEs), which can accelerate software development by suggesting the next …

Statistical language modeling techniques have successfully been applied to large source
code corpora, yielding a variety of new software development tools, such as tools for code …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

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 …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …