A survey on deep learning for software engineering
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 …
and an improved model training method to break the bottleneck of neural network …
A survey of machine learning for big code and naturalness
Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …
engineering has recently taken important steps in proposing learnable probabilistic models …
Competition-level code generation with alphacode
Programming is a powerful and ubiquitous problem-solving tool. Systems that can assist
programmers or even generate programs themselves could make programming more …
programmers or even generate programs themselves could make programming more …
Program synthesis with large language models
This paper explores the limits of the current generation of large language models for
program synthesis in general purpose programming languages. We evaluate a collection of …
program synthesis in general purpose programming languages. We evaluate a collection of …
Cure: Code-aware neural machine translation for automatic program repair
Automatic program repair (APR) is crucial to improve software reliability. Recently, neural
machine translation (NMT) techniques have been used to automatically fix software bugs …
machine translation (NMT) techniques have been used to automatically fix software bugs …
[HTML][HTML] Corporate digital responsibility
We propose that digital technologies and related data become increasingly prevalent and
that, consequently, ethical concerns arise. Looking at four principal stakeholders, we …
that, consequently, ethical concerns arise. Looking at four principal stakeholders, we …
Coconut: combining context-aware neural translation models using ensemble for program repair
Automated generate-and-validate (GV) program repair techniques (APR) typically rely on
hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a …
hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a …
code2seq: Generating sequences from structured representations of code
The ability to generate natural language sequences from source code snippets has a variety
of applications such as code summarization, documentation, and retrieval. Sequence-to …
of applications such as code summarization, documentation, and retrieval. Sequence-to …
Programmatically interpretable reinforcement learning
We present a reinforcement learning framework, called Programmatically Interpretable
Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable …
Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable …
Neurosymbolic programming
We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …
areas of deep learning and program synthesis. Like in classic machine learning, the goal …