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Found 11 papers, 8 papers with code
CodeQueries: A Dataset of Semantic Queries over Code
Compared to the existing datasets, in CodeQueries, the queries are about code semantics, the context is file level and the answers are code spans.
A Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research.
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Machine learning for understanding and editing source code has recently attracted significant interest, with many developments in new models, new code representations, and new tasks. This proliferation can appear disparate and disconnected, making each approach seemingly unique and incompatible, thus obscuring the core machine learning challenges and contributions. In this work, we demonstrate that the landscape can be significantly simplified by taking a general approach of mapping a graph to a sequence of tokens and pointers. Our main result is to show that 16 recently published tasks of different shapes can be cast in this form, based on which a single model architecture achieves near or above state-of-the-art results on nearly all tasks, outperforming custom models like code2seq and alternative generic models like Transformers. This unification further enables multi-task learning and a series of cross-cutting experiments about the importance of different modeling choices for code understanding and repair tasks. The full framework, called PLUR, is easily extensible to more tasks, and will be open-sourced (https://github. com/google-research/plur).
CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation
Designing a suitable representation for code-reasoning tasks is challenging in aspects such as the kinds of program information to model, how to combine them, and how much context to consider.
SpreadsheetCoder: Formula Prediction from Semi-structured Context
In this work, we present the first approach for synthesizing spreadsheet formulas from tabular context, which includes both headers and semi-structured tabular data.
Neural Program Synthesis with a Differentiable Fixer
We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer.
Global Relational Models of Source Code
By studying a popular, non-trivial program repair task, variable-misuse identification, we explore the relative merits of traditional and hybrid model families for code representation.
Learning and Evaluating Contextual Embedding of Source Code
We fine-tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-the-art models, showing that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples.
Pre-trained Contextual Embedding of Source Code
A major advancement in natural-language understanding has been the use of pre-trained token embeddings; BERT and other works have further shown that pre-trained contextual embeddings can be extremely powerful and can be finetuned effectively for a variety of downstream supervised tasks.
Neural Program Repair by Jointly Learning to Localize and Repair
We show that it is beneficial to train a model that jointly and directly localizes and repairs variable-misuse bugs.
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
Our two SPORE algorithms are able to build relationships between responses (e. g., the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable.
