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Found 11 papers, 7 papers with code
Supersonic: Learning to Generate Source Code Optimizations in C/C++
Software optimization refines programs for resource efficiency while preserving functionality.
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).
Multimodal Representation for Neural Code Search
In this paper, to improve the vector space, we introduce tree-serialization methods on a simplified form of AST and build the multimodal representation for the code data.
Ranked #1 on
Code Search
on CodeSearchNet - Ruby
Neural Transfer Learning for Repairing Security Vulnerabilities in C Code
To sum up, this paper shows that transfer learning works well for repairing security vulnerabilities in C compared to learning on a small dataset.
Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities
Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them.
Learning to Fix Build Errors with Graph2Diff Neural Networks
A diff specifies how to modify the code's abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations.
Learning the Relation between Code Features and Code Transforms with Structured Prediction
We instantiate our approach in the context of repair transform prediction for Java programs.
A Literature Study of Embeddings on Source Code
In this survey, we aim to collect and discuss the usage of word embedding techniques on programs and source code.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair
This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning.
The Remarkable Role of Similarity in Redundancy-based Program Repair
Recently, there have been original attempts to use the concept of "code similarity" in program repair, suggesting that similarity analysis has an important role in the repair process.
Software Engineering
The CodRep Machine Learning on Source Code Competition
CodRep is a machine learning competition on source code data.
