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 …
Natural language processing for requirements engineering: A systematic map** study
Natural Language Processing for Requirements Engineering (NLP4RE) is an area of
research and development that seeks to apply natural language processing (NLP) …
research and development that seeks to apply natural language processing (NLP) …
A novel neural source code representation based on abstract syntax tree
Exploiting machine learning techniques for analyzing programs has attracted much
attention. One key problem is how to represent code fragments well for follow-up analysis …
attention. One key problem is how to represent code fragments well for follow-up analysis …
Sysevr: A framework for using deep learning to detect software vulnerabilities
The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
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 …
Wind, solar, and photovoltaic renewable energy systems with and without energy storage optimization: A survey of advanced machine learning and deep learning …
Nowadays, learning-based modeling methods are utilized to build a precise forecast model
for renewable power sources. Computational Intelligence (CI) techniques have been …
for renewable power sources. Computational Intelligence (CI) techniques have been …
Deep semantic feature learning for software defect prediction
Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
Deepfl: Integrating multiple fault diagnosis dimensions for deep fault localization
Learning-based fault localization has been intensively studied recently. Prior studies have
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …
shown that traditional Learning-to-Rank techniques can help precisely diagnose fault …
When deep learning met code search
There have been multiple recent proposals on using deep neural networks for code search
using natural language. Common across these proposals is the idea of embedding code …
using natural language. Common across these proposals is the idea of embedding code …
Automatically generating commit messages from diffs using neural machine translation
Commit messages are a valuable resource in comprehension of software evolution, since
they provide a record of changes such as feature additions and bug repairs. Unfortunately …
they provide a record of changes such as feature additions and bug repairs. Unfortunately …