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Found 9 papers, 9 papers with code
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
The rapid development of large language models has revolutionized code intelligence in software development.
Ranked #11 on
Code Generation
on MBPP
Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
To assure high reliability against numerical defects, in this paper, we propose the RANUM approach including novel techniques for three reliability assurance tasks: detection of potential numerical defects, confirmation of potential-defect feasibility, and suggestion of defect fixes.
Lyra: A Benchmark for Turducken-Style Code Generation
Since a declarative language is typically embedded in an imperative language (i. e., the turducken-style programming) in real-world software development, the promising results on declarative languages can hardly lead to significant reduction of manual software development efforts.
A Syntax-Guided Edit Decoder for Neural Program Repair
Our results show that Recoder repairs 53 bugs on Defects4J v1. 2, which achieves 21. 4% improvement over the previous state-of-the-art approach for single-hunk bugs (TBar).
Generalized Equivariance and Preferential Labeling for GNN Node Classification
Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content.
OCoR: An Overlapping-Aware Code Retriever
To address these problems, we propose a novel neural architecture named OCoR, where we introduce two specifically-designed components to capture overlaps: the first embeds identifiers by character to capture the overlaps between identifiers, and the second introduces a novel overlap matrix to represent the degrees of overlaps between each natural language word and each identifier.
NLocalSAT: Boosting Local Search with Solution Prediction
However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers.
TreeGen: A Tree-Based Transformer Architecture for Code Generation
TreeGen outperformed the previous state-of-the-art approach by 4. 5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89. 1%) and GEO (89. 6%).
A Grammar-Based Structural CNN Decoder for Code Generation
In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation.
