A Neural Network Architecture for Program Understanding Inspired by Human Behaviors (2022.acl-long)
Copied to clipboard
| Challenge: | Existing studies for understanding programs do not take human behaviors as reference. |
| Approach: | They propose a graph neural network model that takes human behaviors as reference in understanding programs. |
| Outcome: | The proposed model performs better on code summarization and code clone detection tasks. |
Similar Papers
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
Copied to clipboard
| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages. |
| Approach: | They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers. |
| Outcome: | The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code. |
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing code summarization approaches ignore the interplay of dependencies among program elements and code hierarchy. |
| Approach: | They propose a code summarization approach utilizing Heterogeneous Code Representations (HCRs) and HierarchyNet. |
| Outcome: | The proposed method improves on existing models and pre-trained models. |
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually. |
| Approach: | They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model . |
| Outcome: | The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST. |
Code Summarization with Structure-induced Transformer (2021.findings-acl)
Copied to clipboard
| Challenge: | Code summarization (CS) is a promising area in recent language understanding . previous work using structurebased traversal or non-sequential models to learn structural program semantics has shown no performance gain . |
| Approach: | They propose to use a structure-based traversal model to learn structural program semantics to generate human language automatically for programming language in the format of source code. |
| Outcome: | Experiments show that the proposed method achieves state-of-the-art on benchmarks. |
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models (D19-3)
Copied to clipboard
| Challenge: | Existing interpretation codebases make it difficult to apply these methods to new models and tasks. |
| Approach: | They propose a framework for interpreting NLP models that provides explanations for specific models. |
| Outcome: | The proposed framework provides interpretation primitives for any AllenNLP model and task, a suite of built-in interpretation methods, and a library of front-end visualization components. |
A Transformer-based Approach for Source Code Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Generating a readable summary that describes the functionality of a program is known as source code summarization. |
| Approach: | They propose a Transformer model that uses a self-attention mechanism to capture long-range dependencies by encoding source code tokens relative to the code token position. |
| Outcome: | The proposed model outperforms the state-of-the-art methods by a significant margin. |
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to generate source code summaries are coarse-grained and noise-filled . however, they do not capture contextual code semantics and are often outdated in continuous software iteration. |
| Approach: | They propose a fine-grained Token-level retrieval-augmented mechanism on the decoder side to enhance performance of neural models. |
| Outcome: | The proposed method produces more low-frequency tokens and is interpretable. |
Augmenting Neural Networks with First-order Logic (P19-1)
Copied to clipboard
| Challenge: | Existing paradigms for training neural networks require large datasets, a paper argues . we present a framework for introducing declarative knowledge to neural networks . |
| Approach: | They propose a framework for introducing declarative knowledge to neural networks . they compile logical statements into graphs that augment a network without extra learnable parameters or manual redesign. |
| Outcome: | The proposed framework improves on three tasks, especially in low-data regimes. |