Charles Sutton

Deep Learning to Detect Redundant Method Comments

2018 · arXiv (Cornell University)

Comments in software are critical for maintenance and reuse. But apart from prescriptive advice, there is little practical support or quantitative understanding of what makes a comment useful. In this paper, we introduce the task …

Learning to Represent Programs with Property Signatures

2020 · arXiv (Cornell University)

We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type $τ_{in}$ and output type $τ_{out}$, a property is …

Suggesting accurate method and class names

2015

Descriptive names are a vital part of readable, and hence maintainable, code. Recent progress on automatically suggesting names for local variables tantalizes with the prospect of replicating that success with method and class names. However, …

Maybe Deep Neural Networks are the Best Choice for Modeling Source Code

2019 · arXiv (Cornell University)

Statistical language modeling techniques have successfully been applied to source code, yielding a variety of new software development tools, such as tools for code suggestion and improving readability. A major issue with these techniques is …

PaLM: Scaling Language Modeling with Pathways

2022 · arXiv (Cornell University)

Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to …