Open AccessProceedings Article
Bimodal Modelling of Source Code and Natural Language
Miltos Allamanis,Daniel Tarlow,Andrew D. Gordon,Yi Wei +3 more
- pp 2123-2132
Reads0
Chats0
TLDR
The aim is to bring together recent work on statistical modelling of source code and work on bimodal models of images and natural language to build probabilistic models that jointly model short natural language utterances and source code snippets.Abstract:
We consider the problem of building probabilistic models that jointly model short natural language utterances and source code snippets. The aim is to bring together recent work on statistical modelling of source code and work on bimodal models of images and natural language. The resulting models are useful for a variety of tasks that involve natural language and source code. We demonstrate their performance on two retrieval tasks: retrieving source code snippets given a natural language query, and retrieving natural language descriptions given a source code query (i.e., source code captioning). Experiments show there to be promise in this direction, and that modelling the structure of source code improves performance.read more
Citations
More filters
Journal ArticleDOI
code2vec: learning distributed representations of code
TL;DR: A neural model for representing snippets of code as continuous distributed vectors as a single fixed-length code vector which can be used to predict semantic properties of the snippet, making it the first to successfully predict method names based on a large, cross-project corpus.
Proceedings ArticleDOI
Summarizing Source Code using a Neural Attention Model
TL;DR: This paper presents the first completely datadriven approach for generating high level summaries of source code, which uses Long Short Term Memory (LSTM) networks with attention to produce sentences that describe C# code snippets and SQL queries.
Proceedings ArticleDOI
Deep code comment generation
TL;DR: DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods.
Posted Content
A Survey of Machine Learning for Big Code and Naturalness
TL;DR: This article presents a taxonomy based on the underlying design principles of each model and uses it to navigate the literature and discuss cross-cutting and application-specific challenges and opportunities.
Journal ArticleDOI
A Survey of Machine Learning for Big Code and Naturalness
TL;DR: A survey of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit the abundance of patterns of code.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Sequence to Sequence Learning with Neural Networks
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
Posted Content
Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
Proceedings Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.
TL;DR: Adaptive subgradient methods as discussed by the authors dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning, which allows us to find needles in haystacks in the form of very predictive but rarely seen features.
Journal Article
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
TL;DR: This work describes and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal functions that can be chosen in hindsight.